Evaluating the promise of inclusion of African ancestry populations in genomics

Analysis of gene expression in the postmortem brain of neurotypical Black Americans reveals contributions of genetic ancestry

Ancestral differences in genomic variation affect the regulation of gene expression; however, most gene expression studies have been limited to European ancestry samples or adjusted to identify ancestry-independent associations. Here, we instead examined the impact of genetic ancestry on gene expression and DNA methylation in the postmortem brain tissue of admixed Black American neurotypical individuals to identify ancestry-dependent and ancestry-independent contributions. Ancestry-associated differentially expressed genes (DEGs), transcripts and gene networks, while notably not implicating neurons, are enriched for genes related to the immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson disease and 30% of heritability for Alzheimer's disease. Ancestry-associated DEGs also show general enrichment for the heritability of diverse immune-related traits but depletion for psychiatric-related traits. We also compared Black and non-Hispanic white Americans, confirming most ancestry-associated DEGs. Our results delineate the extent to which genetic ancestry affects differences in gene expression in the human brain and the implications for brain illness risk.

Polygenic Risk for Type 2 Diabetes in African Americans

African Americans (AAs) have been underrepresented in polygenic risk score (PRS) studies. Here, we integrated genome-wide data from multiple observational studies on type 2 diabetes (T2D), encompassing a total of 101,987 AAs, to train and optimize an AA-focused T2D PRS (PRSAA), using a Bayesian polygenic modeling method. We further tested the score in three independent studies with a total of 7,275 AAs and compared the PRSAA with other published scores. Results show that a 1-SD increase in the PRSAA was associated with 40-60% increase in the odds of T2D (odds ratio [OR] 1.60, 95% CI 1.37-1.88; OR 1.40, 95% CI 1.16-1.70; and OR 1.45, 95% CI 1.30-1.62) across three testing cohorts. These models captured 1.0-2.6% of the variance (R2) in T2D on the liability scale. The positive predictive values for three calculated score thresholds (the top 2%, 5%, and 10%) ranged from 14 to 35%. The PRSAA, in general, performed similarly to existing T2D PRS. The need remains for larger data sets to continue to evaluate the utility of within-ancestry scores in the AA population.

ARTICLE HIGHLIGHTS

Characterizing genetic profiles for high triglyceride levels in U.S. patients of African ancestry

Hypertriglyceridemia (HTG) is a common cardiovascular risk factor characterized by elevated circulating triglyceride (TG) levels. Researchers have assessed the genetic factors that influence HTG in studies focused predominantly on individuals of European ancestry (EA). However, relatively little is known about the contribution of genetic variation to HTG in people of AA, potentially constraining research and treatment opportunities; the lipid profile for African ancestry (AA) populations differs from that of EA populations-which may be partially attributable to genetics. Our objective was to characterize genetic profiles among individuals of AA with mild-to-moderate HTG and severe HTG versus those with normal TGs by leveraging whole genome sequencing (WGS) data and longitudinal electronic health records (EHRs) available in the All of Us (AoU) program. We compared the enrichment of functional variants within five canonical TG metabolism genes, an AA-specific polygenic risk score for TGs, and frequencies of 145 known potentially causal TG variants between patients with HTG and normal TG among a cohort of AA patients (N=15,373). Those with mild-to-moderate HTG (N=342) and severe HTG (N≤20) were more likely to carry APOA5 p.S19W (OR=1.94, 95% CI [1.48-2.54], p=1.63×10-6 and OR=3.65, 95% CI [1.22-10.93], p=0.02, respectively) than those with normal TG. They were also more likely to have an elevated (top 10%) PRS, elevated carriage of potentially causal variant alleles, and carry any genetic risk factor. Alternative definitions of HTG yielded comparable results. In conclusion, individuals of AA with HTG were enriched for genetic risk factors compared to individuals with normal TGs.

Combining Transdiagnostic and Disorder-Level GWAS Enhances Precision of Psychiatric Genetic Risk Profiles in a Multi-Ancestry Sample

The etiology of substance use disorders (SUDs) and psychiatric disorders reflects a combination of both transdiagnostic (i.e., common) and disorder-level (i.e., independent) genetic risk factors. We applied genomic structural equation modeling to examine these genetic factors across SUDs, psychotic, mood, and anxiety disorders using genome-wide association studies (GWAS) of European- (EUR) and African-ancestry (AFR) individuals. In EUR individuals, transdiagnostic genetic factors represented SUDs (143 lead single nucleotide polymorphisms [SNPs]), psychotic (162 lead SNPs), and mood/anxiety disorders (112 lead SNPs). We identified two novel SNPs for mood/anxiety disorders that have probable regulatory roles on FOXP1, NECTIN3, and BTLA genes. In AFR individuals, genetic factors represented SUDs (1 lead SNP) and psychiatric disorders (no significant SNPs). The SUD factor lead SNP, although previously significant in EUR- and cross-ancestry GWAS, is a novel finding in AFR individuals. Shared genetic variance accounted for overlap between SUDs and their psychiatric comorbidities, with second-order GWAS identifying up to 12 SNPs not significantly associated with either first-order factor in EUR individuals. Finally, common and independent genetic effects showed different associations with psychiatric, sociodemographic, and medical phenotypes. For example, the independent components of schizophrenia and bipolar disorder had distinct associations with affective and risk-taking behaviors, and phenome-wide association studies identified medical conditions associated with tobacco use disorder independent of the broader SUDs factor. Thus, combining transdiagnostic and disorder-level genetic approaches can improve our understanding of co-occurring conditions and increase the specificity of genetic discovery, which is critical for psychiatric disorders that demonstrate considerable symptom and etiological overlap.

Overcoming barriers to single-cell RNA sequencing adoption in low- and middle-income countries

The advent of single-cell resolution sequencing and spatial transcriptomics has enabled the delivery of cellular and molecular atlases of tissues and organs, providing new insights into tissue health and disease. However, if the full potential of these technologies is to be equitably realised, ancestrally inclusivity is paramount. Such a goal requires greater inclusion of both researchers and donors in low- and middle-income countries (LMICs). In this perspective, we describe the current landscape of ancestral inclusivity in genomic and single-cell transcriptomic studies. We discuss the collaborative efforts needed to scale the barriers to establishing, expanding, and adopting single-cell sequencing research in LMICs and to enable globally impactful outcomes of these technologies.

Resilience Phenotypes and Psychological Functioning among Individuals with Opioid Use Disorder

BACKGROUND

Opioid use disorder (OUD) is a heterogeneous disorder. However, there is a lack of deep phenotyping investigations focusing on important psychological constructs such as resilience that may impact OUD. The present study aimed to investigate the relationship between trait resilience and the five-factor model of personality (FFM) among individuals with opioid use disorder (OUD). We also explored whether the FFM and trait resilience form specific phenotypes associated with psychological functioning.

METHODS

This secondary analysis of an epigenetic study included participants of African ancestry (n = 72), an understudied population, who met DSM-5 criteria for OUD. Participants completed measures to assess personality traits, trait resilience, current and previous drug use, and psychological functioning (depression, anxiety, and stress).

RESULTS

Linear regression revealed a significant relationship between resilience (CD-RISC-25 score) and the FFM, R2 = 0.56, F(5,62) = 15.7, p<.001. Further, a two-cluster classification emerged as the optimal solution from the cluster analysis. Cluster 1 (n = 33, 45.8% of the sample) showed lower resilience (CD-RISC-25 score: M = 58.6, SD = 11.2) compared to Cluster 2 (n = 35, 48.6%; CD-RISC-25 score: M = 76.1, SD = 11.9). The "High-Resilience Cluster" (Cluster 2) was characterized by higher FFM traits of: Extraversion, Openness, Agreeableness, and Conscientiousness, and lower Neuroticism versus Cluster 1. Multivariate analysis of variance revealed statistically significant differences between the two resilience clusters concerning other psychological symptoms, Λ = 0.732, F(4, 50) = 7.05, p < 0.003.

CONCLUSIONS

These findings suggest associations between the FFM and trait resilience among individuals with OUD. Two distinct "resilience phenotypes" emerged, with high-resilience individuals displaying less stress, anxiety, and depressive symptoms. Results highlight the clinical importance of resilience as a potential target for intervention in people with OUD.

Equity, diversity, and inclusion at the Global Alliance for Genomics and Health

A lack of diversity in genomics for health continues to hinder equitable leadership and access to precision medicine approaches for underrepresented populations. To avoid perpetuating biases within the genomics workforce and genomic data collection practices, equity, diversity, and inclusion (EDI) must be addressed. This paper documents the journey taken by the Global Alliance for Genomics and Health (a genomics-based standard-setting and policy-framing organization) to create a more equitable, diverse, and inclusive environment for its standards and members. Initial steps include the creation of two groups: the Equity, Diversity, and Inclusion Advisory Group and the Regulatory and Ethics Diversity Group. Following a framework that we call "Reflected in our Teams, Reflected in our Standards," both groups address EDI at different stages in their policy development process.

Genetic and environmental contributions to ancestry differences in gene expression in the human brain

Ancestral differences in genomic variation are determining factors in gene regulation; however, most gene expression studies have been limited to European ancestry samples or adjusted for ancestry to identify ancestry-independent associations. We instead examined the impact of genetic ancestry on gene expression and DNA methylation (DNAm) in admixed African/Black American neurotypical individuals to untangle effects of genetic and environmental factors. Ancestry-associated differentially expressed genes (DEGs), transcripts, and gene networks, while notably not implicating neurons, are enriched for genes related to immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson's disease, and 30% of heritability for Alzhemier's disease. Ancestry-associated DEGs also show general enrichment for heritability of diverse immune-related traits but depletion for psychiatric-related traits. The cell-type enrichments and direction of effects vary by brain region. These DEGs are less evolutionarily constrained and are largely explained by genetic variations; roughly 15% are predicted by DNAm variation implicating environmental exposures. We also compared Black and White Americans, confirming most of these ancestry-associated DEGs. Our results highlight how environment and genetic background affect genetic ancestry differences in gene expression in the human brain and affect risk for brain illness.

A pilot study examining the relationship between chronic heroin use and telomere length among individuals of African ancestry

BACKGROUND

Prior research has suggested a possible link between heroin use and shortened telomere length (TL), a marker of cellular aging and genomic stability. We sought to replicate these findings by examining the relationship between TL and heroin use among individuals of African ancestry.

METHODS

This cross-sectional study examined TL among 57 participants [17.5 % female; mean age 48.0 (±6.80) years] of African ancestry with Opioid Use Disorder (OUD) and a mean heroin use duration of 18.2 (±10.7) years. Quantitative polymerase chain reaction (qPCR) was used to calculate TL as the ratio between telomere repeat copy number (T) and a single-copy gene, copy number (S). The primary dependent variable was TL (T/S Ratio) measured in kilobase pairs. Covariates included heroin use years and personality traits. Using a hybrid approach, multiple linear regression and Bayesian linear regression examined the association of chronological age, heroin use years and personality traits with TL.

RESULTS

The multiple linear regression model fit the data well, R2 = 0.265, F(7,49) = 2.53, p < .026. Chronological age (β = -0.36, p = .017), neuroticism (β = 0.46, p = .044), and conscientiousness (β = 0.52, p = .040) were significant predictors of TL. Bayesian linear regression provided moderate support for the alternate hypothesis that chronological age and TL are associated, BF10 = 5.77, R2 = 0.120. The posterior summary of the coefficient was M = 0.719 (SD = 0.278, 95 % credible interval [-1.28, -0.163]).

CONCLUSIONS

Contrary to prior studies, these findings suggest that heroin use duration may not be significantly associated with TL among individuals of African ancestry, highlighting the need for more rigorous research to elucidate the complexity of this relationship.

The promise of data science for health research in Africa

Data science health research promises tremendous benefits for African populations, but its implementation is fraught with substantial ethical governance risks that could thwart the delivery of these anticipated benefits. We discuss emerging efforts to build ethical governance frameworks for data science health research in Africa and the opportunities to advance these through investments by African governments and institutions, international funding organizations and collaborations for research and capacity development.

Leveraging genetic diversity to understand monogenic Parkinson's disease's landscape in AfrAbia

Parkinson's disease may be caused by a single highly deleterious and penetrant pathogenic variant in 5-10% of cases (monogenic). Research into these mutational disorders yields important pathophysiological insights. This article examines the phenotype, genotype, pathophysiology, and geographic and ethnic distribution of genetic forms of disease. Well established Parkinson's disease (PD) causal variants can follow an autosomal dominant (SNCA, LRRK2, and VPS35) and autosomal recessive pattern of inheritance (PRKN, PINK1, and DJ). Parkinson's disease is a worldwide condition, yet the AfrAbia population is understudied in this regard. No prevalence or incidence investigations have been conducted yet. Few studies on genetic risk factors for PD in AfrAbia communities have been reported which supported the notion that the prevalence and incidence rates of PD in AfrAbia are generally lower than those reported for European and North American populations. There have been only a handful of documented genetic studies of PD in AfrAbia and very limited cohort and case-control research studies on PD have been documented. In this article, we provide a summary of prior conducted research on monogenic PD in Africa and highlight data gaps and promising new research directions. We emphasize that monogenic Parkinson's disease is influenced by distinctions in ethnicity and geography, thereby reinforcing the need for global initiatives to aggregate large numbers of patients and identify novel candidate genes. The current article increases our knowledge of the genetics of Parkinson's disease (PD) and helps to further our knowledge on the genetic factors that contribute to PD, such as the lower penetrance and varying clinical expressivity of known genetic variants, particularly in AfrAbian PD patients.

Equitable machine learning counteracts ancestral bias in precision medicine, improving outcomes for all

Gold standard genomic datasets severely under-represent non-European populations, leading to inequities and a limited understanding of human disease [1-8]. Therapeutics and outcomes remain hidden because we lack insights that we could gain from analyzing ancestry-unbiased genomic data. To address this significant gap, we present PhyloFrame, the first-ever machine learning method for equitable genomic precision medicine. PhyloFrame corrects for ancestral bias by integrating big data tissue-specific functional interaction networks, global population variation data, and disease-relevant transcriptomic data. Application of PhyloFrame to breast, thyroid, and uterine cancers shows marked improvements in predictive power across all ancestries, less model overfitting, and a higher likelihood of identifying known cancer-related genes. The ability to provide accurate predictions for underrepresented groups, in particular, is substantially increased. These results demonstrate how AI can mitigate ancestral bias in training data and contribute to equitable representation in medical research.

Performance and accuracy evaluation of reference panels for genotype imputation in sub-Saharan African populations

Based on evaluations of imputation performed on a genotype dataset consisting of about 11,000 sub-Saharan African (SSA) participants, we show Trans-Omics for Precision Medicine (TOPMed) and the African Genome Resource (AGR) to be currently the best panels for imputing SSA datasets. We report notable differences in the number of single-nucleotide polymorphisms (SNPs) that are imputed by different panels in datasets from East, West, and South Africa. Comparisons with a subset of 95 SSA high-coverage whole-genome sequences (WGSs) show that despite being about 20-fold smaller, the AGR imputed dataset has higher concordance with the WGSs. Moreover, the level of concordance between imputed and WGS datasets was strongly influenced by the extent of Khoe-San ancestry in a genome, highlighting the need for integration of not only geographically but also ancestrally diverse WGS data in reference panels for further improvement in imputation of SSA datasets. Approaches that integrate imputed data from different panels could also lead to better imputation.

A pangenome reference of 36 Chinese populations

Human genomics is witnessing an ongoing paradigm shift from a single reference sequence to a pangenome form, but populations of Asian ancestry are underrepresented. Here we present data from the first phase of the Chinese Pangenome Consortium, including a collection of 116 high-quality and haplotype-phased de novo assemblies based on 58 core samples representing 36 minority Chinese ethnic groups. With an average 30.65× high-fidelity long-read sequence coverage, an average contiguity N50 of more than 35.63 megabases and an average total size of 3.01 gigabases, the CPC core assemblies add 189 million base pairs of euchromatic polymorphic sequences and 1,367 protein-coding gene duplications to GRCh38. We identified 15.9 million small variants and 78,072 structural variants, of which 5.9 million small variants and 34,223 structural variants were not reported in a recently released pangenome reference¹. The Chinese Pangenome Consortium data demonstrate a remarkable increase in the discovery of novel and missing sequences when individuals are included from underrepresented minority ethnic groups. The missing reference sequences were enriched with archaic-derived alleles and genes that confer essential functions related to keratinization, response to ultraviolet radiation, DNA repair, immunological responses and lifespan, implying great potential for shedding new light on human evolution and recovering missing heritability in complex disease mapping.

Core issues, case studies, and the need for expanded Legacy African American genomics

Introduction: Genomic studies of Legacy African Americans have a tangled and convoluted history in western science. In this review paper, core issues affecting African American genomic studies are addressed and two case studies, the New York African Burial Ground and the Gullah Geechee peoples, are presented to highlight the current status of genomic research among Africa Americans. Methods: To investigate our target population's core issues, a metadatabase derived from 22 publicly accessible databases were reviewed, evaluated, and synthesized to identify the core bioethical issues prevalent during the centuries of the African American presence in North America. The sequence of metadatabase development included 5 steps: identification of information, record screening and retention of topic relevant information, identification of eligibility via synthesis for concept identifications, and inclusion of studies used for conceptual summaries and studies used for genetic and genomic summaries. To these data we added our emic perspectives and specific insights from our case studies. Results: Overall, there is a paucity of existing research on underrepresent African American genomic diversity. In every category of genomic testing (i.e., diagnostic, clinical predictive, pharmacogenomic, direct-to-consumer, and tumor testing), African Americans are disproportionately underrepresented compared to European Americans. The first of our case studies is from the New York African Burial Ground Project where genomic studies of grave soil derived aDNA yields insights into the causes of death of 17^th and 18^th Century African Americans. In the second of our case studies, research among the Gullah Geechee people of the Carolina Lowcountry reveals a connection between genomic studies and health disparities. Discussion: African Americans have historically borne the brunt of the earliest biomedical studies used to generate and refine primitive concepts in genetics. As exploited victims these investigations, African American men, women, and children were subjected to an ethics-free western science. Now that bioethical safeguards have been added, underrepresented and marginalized people who were once the convenient targets of western science, are now excluded from its health-related benefits. Recommendations to enhance the inclusion of African Americans in global genomic databases and clinical trials should include the following: emphasis on the connection of inclusion to advances in precision medicine, emphasis on the relevance of inclusion to fundamental questions in human evolutionary biology, emphasis on the historical relevance of inclusion for Legacy African Americans, emphasis on the ability of inclusion to foster expanded scientific expertise in the target population, ethical engagement with their descendants, and increase the number of science researchers from these communities.

Toward an open access genomics database of South Africans: ethical considerations

Genomics research holds the potential to improve healthcare. Yet, a very low percentage of the genomic data used in genomics research internationally relates to persons of African origin. Establishing a large-scale, open access genomics database of South Africans may contribute to solving this problem. However, this raises various ethics concerns, including privacy expectations and informed consent. The concept of open consent offers a potential solution to these concerns by (a) being explicit about the research participant's data being in the public domain and the associated privacy risks, and (b) setting a higher-than-usual benchmark for informed consent by making use of the objective assessment of prospective research participants' understanding. Furthermore, in the South African context-where local culture is infused with Ubuntu and its relational view of personhood-community engagement is vital for establishing and maintaining an open access genomics database of South Africans. The South African National Health Research Ethics Council is called upon to provide guidelines for genomics researchers-based on open consent and community engagement-on how to plan and implement open access genomics projects.

Genome-wide Association Identifies Novel Etiological Insights Associated with Parkinson's Disease in African and African Admixed Populations

Background

Understanding the genetic mechanisms underlying diseases in ancestrally diverse populations is a critical step towards the realization of the global application of precision medicine. The African and African admixed populations enable mapping of complex traits given their greater levels of genetic diversity, extensive population substructure, and distinct linkage disequilibrium patterns.

Methods

Here we perform a comprehensive genome-wide assessment of Parkinson's disease (PD) in 197,918 individuals (1,488 cases; 196,430 controls) of African and African admixed ancestry, characterizing population-specific risk, differential haplotype structure and admixture, coding and structural genetic variation and polygenic risk profiling.

Findings

We identified a novel common risk factor for PD and age at onset at the GBA1 locus (risk, rs3115534-G; OR=1.58, 95% CI = 1.37 - 1.80, P=2.397E-14; age at onset, BETA =-2.004, SE =0.57, P = 0.0005), that was found to be rare in non-African/African admixed populations. Downstream short- and long-read whole genome sequencing analyses did not reveal any coding or structural variant underlying the GWAS signal. However, we identified that this signal mediates PD risk via expression quantitative trait locus (eQTL) mechanisms. While previously identified GBA1 associated disease risk variants are coding mutations, here we suggest a novel functional mechanism consistent with a trend in decreasing glucocerebrosidase activity levels. Given the high population frequency of the underlying signal and the phenotypic characteristics of the homozygous carriers, we hypothesize that this variant may not cause Gaucher disease. Additionally, the prevalence of Gaucher's disease in Africa is low.

Interpretation

The present study identifies a novel African-ancestry genetic risk factor in GBA1 as a major mechanistic basis of PD in the African and African admixed populations. This striking result contrasts to previous work in Northern European populations, both in terms of mechanism and attributable risk. This finding highlights the importance of understanding population-specific genetic risk in complex diseases, a particularly crucial point as the field moves toward precision medicine in PD clinical trials and while recognizing the need for equitable inclusion of ancestrally diverse groups in such trials. Given the distinctive genetics of these underrepresented populations, their inclusion represents a valuable step towards insights into novel genetic determinants underlying PD etiology. This opens new avenues towards RNA-based and other therapeutic strategies aimed at reducing lifetime risk.

Research in Context

Evidence Before this Study

Our current understanding of Parkinson's disease (PD) is disproportionately based on studying populations of European ancestry, leading to a significant gap in our knowledge about the genetics, clinical characteristics, and pathophysiology in underrepresented populations. This is particularly notable in individuals of African and African admixed ancestries. Over the last two decades, we have witnessed a revolution in the research area of complex genetic diseases. In the PD field, large-scale genome-wide association studies in the European, Asian, and Latin American populations have identified multiple risk loci associated with disease. These include 78 loci and 90 independent signals associated with PD risk in the European population, nine replicated loci and two novel population-specific signals in the Asian population, and a total of 11 novel loci recently nominated through multi-ancestry GWAS efforts.Nevertheless, the African and African admixed populations remain completely unexplored in the context of PD genetics.

Added Value of this Study

To address the lack of diversity in our research field, this study aimed to conduct the first genome-wide assessment of PD genetics in the African and African admixed populations. Here, we identified a genetic risk factor linked to PD etiology, dissected African-specific differences in risk and age at onset, characterized known genetic risk factors, and highlighted the utility of the African and African admixed risk haplotype substructure for future fine-mapping efforts. We identified a novel disease mechanism via expression changes consistent with decreased GBA1 activity levels. Future large scale single cell expression studies should investigate the neuronal populations in which expression differences are most prominent. This novel mechanism may hold promise for future efficient RNA-based therapeutic strategies such as antisense oligonucleotides or short interfering RNAs aimed at preventing and decreasing disease risk. We envisage that these data generated under the umbrella of the Global Parkinson's Genetics Program (GP2) will shed light on the molecular mechanisms involved in the disease process and might pave the way for future clinical trials and therapeutic interventions. This work represents a valuable resource in an underserved population, supporting pioneering research within GP2 and beyond. Deciphering causal and genetic risk factors in all these ancestries will help determine whether interventions, potential targets for disease modifying treatment, and prevention strategies that are being studied in the European populations are relevant to the African and African admixed populations.

Implications of all the Available Evidence

We nominate a novel signal impacting GBA1 as the major genetic risk factor for PD in the African and African admixed populations. The present study could inform future GBA1 clinical trials, improving patient stratification. In this regard, genetic testing can help to design trials likely to provide meaningful and actionable answers. It is our hope that these findings may ultimately have clinical utility for this underrepresented population.

Genetic Risk, Neighborhood Characteristics, and Behavioral Difficulties Among African American Adolescents Living in Very Low-Income Neighborhoods

Behavioral difficulties among African American youth are disproportionately detrimental to their future well-being compared to when demonstrated by White American youth. The majority of gene-environment studies of behavior have been conducted with European ancestry samples, limiting our knowledge of these processes among African Americans. This study examined the influence of positive and negative neighborhood conditions, in the context of genetic risk, on behavioral difficulties among low-income African American adolescents. Data were from the Genes, Environment, and Neighborhood Initiative study of African American youth in high-poverty neighborhoods, n = 524, M age = 15.89, SD = 1.42. DNA samples were collected using the Oragene Discovery 500 series, and polygenic risk scores for behavioral difficulties computed. Neighborhood informal social control, social cohesion, physical disorder, and social disorder were assessed. Adolescent alcohol use, hyperactivity/inattention and conduct problems were examined as outcomes. After controlling for polygenic risk, lower levels of neighborhood social disorder and higher levels of social cohesion were associated with fewer youth-reported hyperactivity/inattention and conduct problems. Less social disorder also was associated with fewer parent-reported behavioral difficulties. Neighborhood characteristics did not moderate associations between genetic risk and the outcomes. Higher levels of positive and lower levels of negative neighborhood characteristics can be associated with lower levels of behavioral difficulties among African American youth living in poverty, even after taking into account genetic risk.

Low and differential polygenic score generalizability among African populations due largely to genetic diversity

African populations are vastly underrepresented in genetic studies but have the most genetic variation and face wide-ranging environmental exposures globally. Because systematic evaluations of genetic prediction had not yet been conducted in ancestries that span African diversity, we calculated polygenic risk scores (PRSs) in simulations across Africa and in empirical data from South Africa, Uganda, and the United Kingdom to better understand the generalizability of genetic studies. PRS accuracy improves with ancestry-matched discovery cohorts more than from ancestry-mismatched studies. Within ancestrally and ethnically diverse South African individuals, we find that PRS accuracy is low for all traits but varies across groups. Differences in African ancestries contribute more to variability in PRS accuracy than other large cohort differences considered between individuals in the United Kingdom versus Uganda. We computed PRS in African ancestry populations using existing European-only versus ancestrally diverse genetic studies; the increased diversity produced the largest accuracy gains for hemoglobin concentration and white blood cell count, reflecting large-effect ancestry-enriched variants in genes known to influence sickle cell anemia and the allergic response, respectively. Differences in PRS accuracy across African ancestries originating from diverse regions are as large as across out-of-Africa continental ancestries, requiring commensurate nuance.

Nutrigenomics in the context of evolution

Nutrigenomics describes the interaction between nutrients and our genome. Since the origin of our species most of these nutrient-gene communication pathways have not changed. However, our genome experienced over the past 50,000 years a number of evolutionary pressures, which are based on the migration to new environments concerning geography and climate, the transition from hunter-gatherers to farmers including the zoonotic transfer of many pathogenic microbes and the rather recent change of societies to a preferentially sedentary lifestyle and the dominance of Western diet. Human populations responded to these challenges not only by specific anthropometric adaptations, such as skin color and body stature, but also through diversity in dietary intake and different resistance to complex diseases like the metabolic syndrome, cancer and immune disorders. The genetic basis of this adaptation process has been investigated by whole genome genotyping and sequencing including that of DNA extracted from ancient bones. In addition to genomic changes, also the programming of epigenomes in pre- and postnatal phases of life has an important contribution to the response to environmental changes. Thus, insight into the variation of our (epi)genome in the context of our individual's risk for developing complex diseases, helps to understand the evolutionary basis how and why we become ill. This review will discuss the relation of diet, modern environment and our (epi)genome including aspects of redox biology. This has numerous implications for the interpretation of the risks for disease and their prevention.

Anti-racist strategies for clinical and translational research: Design, implementation, and lessons learned from a new course

Translational research should examine racism and bias and improve health equity. We designed and implemented a course for the Master of Science in Clinical Investigation program of the Northwestern University Clinical and Translational Sciences Institute. We describe curriculum development, content, outcomes, and revisions involving 36 students in 2 years of "Anti-Racist Strategies for Clinical and Translational Science." Ninety-six percent of students reported they would recommend the course. Many reported changes in research approaches based on course content. A course designed to teach anti-racist research design is feasible and has a positive short-term impact on learners.

Gene-environment interactions and their impact on human health

The molecular processes underlying human health and disease are highly complex. Often, genetic and environmental factors contribute to a given disease or phenotype in a non-additive manner, yielding a gene-environment (G × E) interaction. In this work, we broadly review current knowledge on the impact of gene-environment interactions on human health. We first explain the independent impact of genetic variation and the environment. We next detail well-established G × E interactions that impact human health involving environmental toxicants, pollution, viruses, and sex chromosome composition. We conclude with possibilities and challenges for studying G × E interactions.

Human height: a model common complex trait

CONTEXT

Like other complex phenotypes, human height reflects a combination of environmental and genetic factors, but is notable for being exceptionally easy to measure. Height has therefore been commonly used to make observations later generalised to other phenotypes though the appropriateness of such generalisations is not always considered.

OBJECTIVES

We aimed to assess height's suitability as a model for other complex phenotypes and review recent advances in height genetics with regard to their implications for complex phenotypes more broadly.

METHODS

We conducted a comprehensive literature search in PubMed and Google Scholar for articles relevant to the genetics of height and its comparatibility to other phenotypes.

RESULTS

Height is broadly similar to other phenotypes apart from its high heritability and ease of measurment. Recent genome-wide association studies (GWAS) have identified over 12,000 independent signals associated with height and saturated height's common single nucleotide polymorphism based heritability of height within a subset of the genome in individuals similar to European reference populations.

CONCLUSIONS

Given the similarity of height to other complex traits, the saturation of GWAS's ability to discover additional height-associated variants signals potential limitations to the omnigenic model of complex-phenotype inheritance, indicating the likely future power of polygenic scores and risk scores, and highlights the increasing need for large-scale variant-to-gene mapping efforts.

Genetic regulators of cytokine responses upon BCG vaccination in children from West Africa

Genetic variation is a key factor influencing cytokine production capacity, but which genetic loci regulate cytokine production before and after vaccination, particularly in African population is unknown. Here, we aimed to identify single-nucleotide polymorphisms (SNPs) controlling cytokine responses (cQTLs) after microbial stimulation in infants of West-African ancestry, comprising of low-birth-weight neonates randomized to bacillus Calmette-Guérin (BCG) vaccine-at-birth (intervention) or to the usual delayed BCG (control). Genome-wide cytokine QTL mapping revealed 12 independent cQTLs loci, of which the LINC01082-LINC00917 locus influenced more than half of the cytokine-stimulation pairs assessed. Furthermore, nine distinct cQTLs were found among infants randomized to BCG. Functional validation confirmed that several complement genes affect cytokine response after BCG vaccination. We observed a limited overlap of common cQTLs between the West-African infants and cohorts of Western European individuals. These data reveal strong population-specific genetic effects on cytokine production and may indicate new opportunities for therapeutic intervention and vaccine development in African populations.

Attitudes and beliefs regarding race-targeted genetic testing of Black people: A systematic review

Geographical ancestry has been associated with an increased risk of various genetic conditions. Race and ethnicity often have been used as proxies for geographical ancestry. Despite numerous problems associated with the crude reliance on race and ethnicity as proxies for geographical ancestry, some genetic testing in the clinical, research, and employment settings has been and continues to be race- or ethnicity-based. Race-based or race-targeted genetic testing refers to genetic testing offered only or primarily to people of particular racial or ethnic groups because of presumed differences among groups. One current example is APOL1 testing of Black kidney donors. Race-based genetic testing raises numerous ethical and policy questions. Given the ongoing reliance on the Black race in genetic testing, it is important to understand the views of people who identify as Black or are identified as Black (including African American, Afro-Caribbean, and Hispanic Black) regarding race-based genetic testing that targets Black people because of their race. We conducted a systematic review of studies and reports of stakeholder-engaged projects that examined how people who identify as or are identified as Black perceive genetic testing that specifically presumes genetic differences exist among racial groups or uses race as a surrogate for ancestral genetic variation and targets Black people. Our review identified 14 studies that explicitly studied this question and another 13 that implicitly or tacitly studied this matter. We found four main factors that contribute to a positive attitude toward race-targeted genetic testing (facilitators) and eight main factors that are associated with concerns regarding race-targeted genetic testing (barriers). This review fills an important gap. These findings should inform future genetic research and the policies and practices developed in clinical, research, public health, or other settings regarding genetic testing.

Fast, accurate, and racially unbiased pan-cancer tumor-only variant calling with tabular machine learning

Accurately identifying somatic mutations is essential for precision oncology and crucial for calculating tumor-mutational burden (TMB), an important predictor of response to immunotherapy. For tumor-only variant calling (i.e., when the cancer biopsy but not the patient's normal tissue sample is sequenced), accurately distinguishing somatic mutations from germline variants is a challenging problem that, when unaddressed, results in unreliable, biased, and inflated TMB estimates. Here, we apply machine learning to the task of somatic vs germline classification in tumor-only solid tumor samples using TabNet, XGBoost, and LightGBM, three machine-learning models for tabular data. We constructed a training set for supervised classification using features derived exclusively from tumor-only variant calling and drawing somatic and germline truth labels from an independent pipeline using the patient-matched normal samples. All three trained models achieved state-of-the-art performance on two holdout test datasets: a TCGA dataset including sarcoma, breast adenocarcinoma, and endometrial carcinoma samples (AUC > 94%), and a metastatic melanoma dataset (AUC > 85%). Concordance between matched-normal and tumor-only TMB improves from R2 = 0.006 to 0.71-0.76 with the addition of a machine-learning classifier, with LightGBM performing best. Notably, these machine-learning models generalize across cancer subtypes and capture kits with a call rate of 100%. We reproduce the recent finding that tumor-only TMB estimates for Black patients are extremely inflated relative to that of white patients due to the racial biases of germline databases. We show that our approach with XGBoost and LightGBM eliminates this significant racial bias in tumor-only variant calling.

Extensive set of African ancestry-informative markers (AIMs) to study ancestry and population health

Introduction: Human populations are often highly structured due to differences in genetic ancestry among groups, posing difficulties in associating genes with diseases. Ancestry-informative markers (AIMs) aid in the detection of population stratification and provide an alternative approach to map population-specific alleles to disease. Here, we identify and characterize a novel set of African AIMs that separate populations of African ancestry from other global populations including those of European ancestry. Methods: Using data from the 1000 Genomes Project, highly informative SNP markers from five African subpopulations were selected based on estimates of informativeness (In) and compared against the European population to generate a final set of 46,737 African ancestry-informative markers (AIMs). The AIMs identified were validated using an independent set and functionally annotated using tools like SIFT, PolyPhen. They were also investigated for representation of commonly used SNP arrays. Results: This set of African AIMs effectively separates populations of African ancestry from other global populations and further identifies substructure between populations of African ancestry. When a subset of these AIMs was studied in an independent dataset, they differentiated people who self-identify as African American or Black from those who identify their ancestry as primarily European. Most of the AIMs were found to be in their intergenic and intronic regions with only 0.6% in the coding regions of the genome. Most of the commonly used SNP array investigated contained less than 10% of the AIMs. Discussion: While several functional annotations of both coding and non-coding African AIMs are supported by the literature and linked these high-frequency African alleles to diseases in African populations, more effort is needed to map genes to diseases in these genetically diverse subpopulations. The relative dearth of these African AIMs on current genotyping platforms (the array with the highest fraction, llumina's Omni 5, harbors less than a quarter of AIMs), further demonstrates a greater need to better represent historically understudied populations.

"Extremely slow and capricious": A qualitative exploration of genetic researcher priorities in selecting shared data resources

PURPOSE

Genetic researchers' selection of a database can have scientific, regulatory, and ethical implications. It is important to understand what is driving database selection such that database stewards can be responsive to user needs while balancing the interests of communities in equitably benefiting from advances.

METHODS

We conducted 23 semistructured interviews with US academic genetic researchers working with private, government, and collaboratory data stewards to explore factors that they consider when selecting a genetic database.

RESULTS

Interviewees used existing databases to avoid burdens of primary data collection, which was described as expensive and time-consuming. They highlighted ease of access as the most important selection factor, integrating concepts of familiarity and efficiency. Data features, such as size and available phenotype, were also important. Demographic diversity was not originally cited by any interviewee as a pivotal factor; when probed, most stated that the option to consider diversity in database selection was limited. Database features, including integrity, harmonization, and storage were also described as key components of efficient use.

CONCLUSION

There is a growing market and competition between genetic data stewards. Data need to be accessible, harmonized, and administratively supported for their existence to be translated into use and, in turn, result in scientific advancements across diverse communities.

East Africa International Center of Excellence for Malaria Research: Summary of Key Research Findings

The Program for Resistance, Immunology, Surveillance, and Modeling of Malaria (PRISM) has been conducting malaria research in Uganda since 2010 to improve the understanding of the disease and measure the impact of population-level control interventions in the country. Here, we will summarize key research findings from a series of studies addressing routine health facility-based surveillance, comprehensive cohort studies, studies of the molecular epidemiology, and transmission of malaria, evaluation of antimalarial drug efficacy, and resistance across the country, and assessments of insecticide resistance. Among our key findings are the following. First, we found that in historically high transmission areas of Uganda, a combination of universal distribution of long-lasting insecticidal-treated nets (LLINs) and sustained indoor residual spraying (IRS) of insecticides lowered the malaria burden greatly, but marked resurgences occurred if IRS was discontinued. Second, submicroscopic infections are common and key drivers of malaria transmission, especially in school-age children (5-15 years). Third, markers of drug resistance have changed over time, with new concerning emergence of markers predicting resistance to artemisinin antimalarials. Fourth, insecticide resistance monitoring has demonstrated high levels of resistance to pyrethroids, appreciable impact of the synergist piperonyl butoxide to pyrethroid susceptibility, emerging resistance to carbamates, and complete susceptibility of malaria vectors to organophosphates, which could have important implications for vector control interventions. Overall, PRISM has yielded a wealth of information informing researchers and policy-makers on the malaria burden and opportunities for improved malaria control and eventual elimination in Uganda. Continued studies concerning all the types of surveillance discussed above are ongoing.

Coronavirus Host Genomics Study: South Africa (COVIGen-SA)

Host genetic factors are known to modify the susceptibility, severity, and outcomes of COVID-19 and vary across populations. However, continental Africans are yet to be adequately represented in such studies despite the importance of genetic factors in understanding Africa's response to the pandemic. We describe the development of a research resource for coronavirus host genomics studies in South Africa known as COVIGen-SA-a multicollaborator strategic partnership designed to provide harmonised demographic, clinical, and genetic information specific to Black South Africans with COVID-19. Over 2,000 participants have been recruited to date. Preliminary results on 1,354 SARS-CoV-2 positive participants from four participating studies showed that 64.7% were female, 333 had severe disease, and 329 were people living with HIV. Through this resource, we aim to provide insights into host genetic factors relevant to African-ancestry populations, using both genome-wide association testing and targeted sequencing of important genomic loci. This project will promote and enhance partnerships, build skills, and develop resources needed to address the COVID-19 burden and associated risk factors in South African communities.

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.

Targeting Representation: Interpreting Calls for Diversity in Precision Medicine Research

Scientists have identified a "diversity gap" in genetic samples and health data, which have been drawn predominantly from individuals of European ancestry, as posing an existential threat to the promise of precision medicine. Inadequate inclusion as articulated by scientists, policymakers, and ethicists has prompted large-scale initiatives aimed at recruiting populations historically underrepresented in biomedical research. Despite explicit calls to increase diversity, the meaning of diversity - which dimensions matter for what outcomes and why - remain strikingly imprecise. Drawing on our document review and qualitative data from observations and interviews of funders and research teams involved in five precision medicine research (PMR) projects, we note that calls for increasing diversity often focus on "representation" as the goal of recruitment. The language of representation is used flexibly to refer to two objectives: achieving sufficient genetic variation across populations and including historically disenfranchised groups in research. We argue that these dual understandings of representation are more than rhetorical slippage, but rather allow for the contemporary collection of samples and data from marginalized populations to stand in as correcting historical exclusion of social groups towards addressing health inequity. We trace the unresolved historical debates over how and to what extent researchers should procure diversity in PMR and how they contributed to ongoing uncertainty about what axes of diversity matter and why. We argue that ambiguity in the meaning of representation at the outset of a study contributes to a lack of clear conceptualization of diversity downstream throughout subsequent phases of the study.

Serum lead, mercury, manganese, and copper and DNA methylation age among adults in Detroit, Michigan

Although the effects of lead, mercury, manganese, and copper on individual disease processes are well understood, estimating the health effects of long-term exposure to these metals at the low concentrations often observed in the general population is difficult. In addition, the health effects of joint exposure to multiple metals are difficult to estimate. Biological aging refers to the integrative progression of multiple physiologic and molecular changes that make individuals more at risk of disease. Biomarkers of biological aging may be useful to estimate the population-level effects of metal exposure prior to the development of disease in the population. We used data from 290 participants in the Detroit Neighborhood Health Study to estimate the effect of serum lead, mercury, manganese, and copper on three DNA methylation-based biomarkers of biological aging (Horvath Age, PhenoAge, and GrimAge). We used mixed models and Bayesian kernel machine regression and controlled for participant sex, race, ethnicity, cigarette use, income, educational attainment, and block group poverty. We observed consistently positive estimates of the effects between lead and GrimAge acceleration and mercury and PhenoAge acceleration. In contrast, we observed consistently negative associations between manganese and PhenoAge acceleration and mercury and Horvath Age acceleration. We also observed curvilinear relationships between copper and both PhenoAge and GrimAge acceleration. Increasing total exposure to the observed mixture of metals was associated with increased PhenoAge and GrimAge acceleration and decreased Horvath Age acceleration. These findings indicate that an increase in serum lead or mercury from the 25th to 75th percentile is associated with a ∼0.25-year increase in two epigenetic markers of all-cause mortality in a population of adults in Detroit, Michigan. While few of the findings were statistically significant, their consistency and novelty warrant interest.

Testing the generalizability of ancestry-specific polygenic risk scores to predict prostate cancer in sub-Saharan Africa

BACKGROUND

Genome-wide association studies do not always replicate well across populations, limiting the generalizability of polygenic risk scores (PRS). Despite higher incidence and mortality rates of prostate cancer in men of African descent, much of what is known about cancer genetics comes from populations of European descent. To understand how well genetic predictions perform in different populations, we evaluated test characteristics of PRS from three previous studies using data from the UK Biobank and a novel dataset of 1298 prostate cancer cases and 1333 controls from Ghana, Nigeria, Senegal, and South Africa.

RESULTS

Allele frequency differences cause predicted risks of prostate cancer to vary across populations. However, natural selection is not the primary driver of these differences. Comparing continental datasets, we find that polygenic predictions of case vs. control status are more effective for European individuals (AUC 0.608-0.707, OR 2.37-5.71) than for African individuals (AUC 0.502-0.585, OR 0.95-2.01). Furthermore, PRS that leverage information from African Americans yield modest AUC and odds ratio improvements for sub-Saharan African individuals. These improvements were larger for West Africans than for South Africans. Finally, we find that existing PRS are largely unable to predict whether African individuals develop aggressive forms of prostate cancer, as specified by higher tumor stages or Gleason scores.

CONCLUSIONS

Genetic predictions of prostate cancer perform poorly if the study sample does not match the ancestry of the original GWAS. PRS built from European GWAS may be inadequate for application in non-European populations and perpetuate existing health disparities.

Towards a global view of multiple sclerosis genetics

Multiple sclerosis (MS) is a neuroimmunological disorder of the CNS with a strong heritable component. The genetic architecture of MS susceptibility is well understood in populations of European ancestry. However, the extent to which this architecture explains MS susceptibility in populations of non-European ancestry remains unclear. In this Perspective article, we outline the scientific arguments for studying MS genetics in ancestrally diverse populations. We argue that this approach is likely to yield insights that could benefit individuals with MS from all ancestral groups. We explore the logistical and theoretical challenges that have held back this field to date and conclude that, despite these challenges, inclusion of participants of non-European ancestry in MS genetics studies will ultimately be of value to all patients with MS worldwide.

Pan-African genome demonstrates how population-specific genome graphs improve high-throughput sequencing data analysis

Graph-based genome reference representations have seen significant development, motivated by the inadequacy of the current human genome reference to represent the diverse genetic information from different human populations and its inability to maintain the same level of accuracy for non-European ancestries. While there have been many efforts to develop computationally efficient graph-based toolkits for NGS read alignment and variant calling, methods to curate genomic variants and subsequently construct genome graphs remain an understudied problem that inevitably determines the effectiveness of the overall bioinformatics pipeline. In this study, we discuss obstacles encountered during graph construction and propose methods for sample selection based on population diversity, graph augmentation with structural variants and resolution of graph reference ambiguity caused by information overload. Moreover, we present the case for iteratively augmenting tailored genome graphs for targeted populations and demonstrate this approach on the whole-genome samples of African ancestry. Our results show that population-specific graphs, as more representative alternatives to linear or generic graph references, can achieve significantly lower read mapping errors and enhanced variant calling sensitivity, in addition to providing the improvements of joint variant calling without the need of computationally intensive post-processing steps.

Molecular Variants in Genes related to the Response to Ocular Hypotensive Drugs in an Afro-Colombian Population

Aims:

This study aimed to conduct an exploratory analysis of the pharmacogenomic variants involved in ocular hypotensive drugs to understand the individual differential response in an Afro-descendant population.

Background:

Glaucoma is the leading cause of irreversible blindness worldwide. The pharmacologic treatment available consists of lowering intraocular pressure by administering topical drugs. In Asian and Caucasian people, pharmacogenomic variants associated with the efficacy of these treatments have been identified. However, in Afro-descendant populations, there is a profound gap in this knowledge.

Objective:

This study identified the pharmacogenomic variants related to ocular hypotensive efficacy treatment in Afro-descendant individuals from the Archipelago of San Andres and Providence, Colombia.

Methods:

An analysis of whole-exome sequencings (WES), functional annotation, and clinical significance was performed for pharmacogenomic variants reported in PharmGKB databases; in turn, an in silico available prediction analysis was carried out for the novel variants.

Results:

We identified six out of 18 non-synonymous variants with a clinical annotation in PharmGKB. Five were classified as level three evidence for the hypotensive drugs; rs1801252 and rs1801253 in the ADRB1 gene and rs1042714 in the ADRB2 gene. These pharmacogenomic variants have been involved in a lack of efficacy of topical beta-blockers and higher systolic and diastolic pressure under treatment with ophthalmic timolol drug. The rs1045642 in the ABCB1 gene was associated with greater efficacy of treatments with latanoprost drug. Also, we found the haplotypes *17 for CYP2D6 and *10 for CYP2C19; both related to reducing the enzyme activity to timolol drug metabolization. In addition, we observed 50 novel potentially actionable variants; 36 synonymous, two insertion variants that caused frameshift mutations, and 12 non-synonymous, where five were predicted to be pathogenic based on several pathogenicity predictions.

Conclusion:

Our results suggested that the pharmacogenomic variants were found to decrease the ocular hypotensive efficacy treatment in a Colombian Afro-descendant population and revealed a significant proportion of novel variants with a potential to influence drug response.

Mitochondrial DNA variation in Parkinson’s disease: Analysis of “out-of-place” population variants as a risk factor

Mitochondrial DNA (mtDNA), a potential source of mitochondrial dysfunction, has been implicated in Parkinson’s disease (PD). However, many previous studies investigating associations between mtDNA population variation and PD reported inconsistent or contradictory findings. Here, we investigated an alternative hypothesis to determine whether mtDNA variation could play a significant role in PD risk. Emerging evidence suggests that haplogroup-defining mtDNA variants may have pathogenic potential if they occur “out-of-place” on a different maternal lineage. We hypothesized that the mtDNA of PD cases would be enriched for out-of-place variation in genes encoding components of the oxidative phosphorylation complexes. We tested this hypothesis with a unique dataset comprising whole mitochondrial genomes of 70 African ancestry PD cases, two African ancestry control groups (n = 78 and n = 53) and a replication group of 281 European ancestry PD cases and 140 controls from the Parkinson’s Progression Markers Initiative cohort. Significantly more African ancestry PD cases had out-of-place variants than controls from the second control group (P < 0.0125), although this association was not observed in the first control group nor the replication group. As the first mtDNA study to include African ancestry PD cases and to explore out-of-place variation in a PD context, we found evidence that such variation might be significant in this context, thereby warranting further replication in larger cohorts.

Transferability of genetic risk scores in African populations

The poor transferability of genetic risk scores (GRSs) derived from European ancestry data in diverse populations is a cause of concern. We set out to evaluate whether GRSs derived from data of African American individuals and multiancestry data perform better in sub-Saharan Africa (SSA) compared to European ancestry-derived scores. Using summary statistics from the Million Veteran Program (MVP), we showed that GRSs derived from data of African American individuals enhance polygenic prediction of lipid traits in SSA compared to European and multiancestry scores. However, our GRS prediction varied greatly within SSA between the South African Zulu (low-density lipoprotein cholesterol (LDL-C), R2 = 8.14%) and Ugandan cohorts (LDL-C, R2 = 0.026%). We postulate that differences in the genetic and environmental factors between these population groups might lead to the poor transferability of GRSs within SSA. More effort is required to optimize polygenic prediction in Africa.

Genetic differentiation in East African ethnicities and its relationship with endurance running success

Since the 1960s, East African athletes, mainly from Kenya and Ethiopia, have dominated long-distance running events in both the male and female categories. Further demographic studies have shown that two ethnic groups are overrepresented among elite endurance runners in each of these countries: the Kalenjin, from Kenya, and the Oromo, from Ethiopia, raising the possibility that this dominance results from genetic or/and cultural factors. However, looking at the life history of these athletes or at loci previously associated with endurance athletic performance, no compelling explanation has emerged. Here, we used a population approach to identify peaks of genetic differentiation for these two ethnicities and compared the list of genes close to these regions with a list, manually curated by us, of genes that have been associated with traits possibly relevant to endurance running in GWAS studies, and found a significant enrichment in both populations (Kalenjin, P = 0.048, and Oromo, P = 1.6x10^-5). Those traits are mainly related to anthropometry, circulatory and respiratory systems, energy metabolism, and calcium homeostasis. Our results reinforce the notion that endurance running is a systemic activity with a complex genetic architecture, and indicate new candidate genes for future studies. Finally, we argue that a deterministic relationship between genetics and sports must be avoided, as it is both scientifically incorrect and prone to reinforcing population (racial) stereotyping.

Opportunities and challenges for the use of common controls in sequencing studies

Genome-wide association studies using large-scale genome and exome sequencing data have become increasingly valuable in identifying associations between genetic variants and disease, transforming basic research and translational medicine. However, this progress has not been equally shared across all people and conditions, in part due to limited resources. Leveraging publicly available sequencing data as external common controls, rather than sequencing new controls for every study, can better allocate resources by augmenting control sample sizes or providing controls where none existed. However, common control studies must be carefully planned and executed as even small differences in sample ascertainment and processing can result in substantial bias. Here, we discuss challenges and opportunities for the robust use of common controls in high-throughput sequencing studies, including study design, quality control and statistical approaches. Thoughtful generation and use of large and valuable genetic sequencing data sets will enable investigation of a broader and more representative set of conditions, environments and genetic ancestries than otherwise possible.

Meta-analysis of sub-Saharan African studies provides insights into genetic architecture of lipid traits

Genetic associations for lipid traits have identified hundreds of variants with clear differences across European, Asian and African studies. Based on a sub-Saharan-African GWAS for lipid traits in the population cross-sectional AWI-Gen cohort (N = 10,603) we report a novel LDL-C association in the GATB region (P-value=1.56 × 10⁻⁸). Meta-analysis with four other African cohorts (N = 23,718) provides supporting evidence for the LDL-C association with the GATB/FHIP1A region and identifies a novel triglyceride association signal close to the FHIT gene (P-value =2.66 × 10⁻⁸). Our data enable fine-mapping of several well-known lipid-trait loci including LDLR, PMFBP1 and LPA. The transferability of signals detected in two large global studies (GLGC and PAGE) consistently improves with an increase in the size of the African replication cohort. Polygenic risk score analysis shows increased predictive accuracy for LDL-C levels with the narrowing of genetic distance between the discovery dataset and our cohort. Novel discovery is enhanced with the inclusion of African data.

Genetic associations and polygenic scores for lipid traits have low transferability to African individuals. Here, the authors perform a large sub-Sarahan African lipid GWAS and find that larger datasets and better global representation in discovery GWAS help to bridge this gap.

Lower Exome Sequencing Coverage of Ancestrally African Patients in The Cancer Genome Atlas

BACKGROUND

In the United States, cancer disproportionately impacts Black and African American individuals. Identifying genetic factors underlying cancer disparities has been an important research focus and requires data that are equitable in both quantity and quality across racial groups. It is widely recognized that DNA databases quantitatively underrepresent minorities. However, the differences in data quality between racial groups have not been well studied.

METHODS

We compared the qualities of germline and tumor exomes between ancestrally African and European patients in The Cancer Genome Atlas of 7 cancers with at least 50 self-reported Black patients in the context of sequencing depth, tumor purity, and qualities of germline variants and somatic mutations.

RESULTS

Germline and tumor exomes from ancestrally African patients were sequenced at statistically significantly lower depth in 6 out of the 7 cancers. For 3 cancers, most ancestrally European exomes were sequenced in early sample batches at higher depth, whereas ancestrally African exomes were concentrated in later batches and sequenced at much lower depth. For the other 3 cancers, the reasons of lower sequencing coverage of ancestrally African exomes remain unknown. Furthermore, even when the sequencing depths were comparable, African exomes had disproportionally higher percentages of positions with insufficient coverage, likely because of the known European bias in the human reference genome that impacted exome capture kit design.

CONCLUSIONS

Overall and positional lower sequencing depths of ancestrally African exomes in The Cancer Genome Atlas led to underdetection and lower quality of variants, highlighting the need to consider epidemiological factors for future genomics studies.

The Promoting Equity in Stem Cell Genomics Survey

Aim: This study aimed to determine knowledge and attitudes toward induced pluripotent stem cell technology and biobanking. Methods: A survey instrument was developed to determine individuals' knowledge of and attitudes toward these technologies. Results: Results from 276 ethnically diverse participants who took the online survey demonstrated significant associations (p ≤ 0. 05) in knowledge by ethnicity and race regarding properties of stem cells, different types of stem cells and previous sample donation behavior. Significantly more Whites 39% (n = 53) compared with Blacks or African-Americans 19.2% (n = 14) had previous knowledge of induced pluripotent stem cells (χ² = 8.544; p = 0.003) Conclusion: Overall, White race was associated with greater knowledge about stem cells and biobanks and greater willingness to donate samples for future research.

A functional genomics approach in Tanzanian population identifies distinct genetic regulators of cytokine production compared to European population

Humans exhibit remarkable interindividual and interpopulation immune response variability upon microbial challenges. Cytokines play a vital role in regulating inflammation and immune responses, but dysregulation of cytokine responses has been implicated in different disease states. Host genetic factors were previously shown to significantly impact cytokine response heterogeneity mainly in European-based studies, but it is unclear whether these findings are transferable to non-European individuals. Here, we aimed to identify genetic variants modulating cytokine responses in healthy adults of East African ancestry from Tanzania. We leveraged both cytokine and genetic data and performed genome-wide cytokine quantitative trait loci (cQTLs) mapping. The results were compared with another cohort of healthy adults of Western European ancestry via direct overlap and functional enrichment analyses. We also performed meta-analyses to identify cQTLs with congruent effect direction in both populations. In the Tanzanians, cQTL mapping identified 80 independent suggestive loci and one genome-wide significant locus (TBC1D22A) at chromosome 22; SNP rs12169244 was associated with IL-1b release after Salmonella enteritidis stimulation. Remarkably, the identified cQTLs varied significantly when compared to the European cohort, and there was a very limited percentage of overlap (1.6% to 1.9%). We further observed ancestry-specific pathways regulating induced cytokine responses, and there was significant enrichment of the interferon pathway specifically in the Tanzanians. Furthermore, contrary to the Europeans, genetic variants in the TLR10-TLR1-TLR6 locus showed no effect on cytokine response. Our data reveal both ancestry-specific effects of genetic variants and pathways on cytokine response heterogeneity, hence arguing for the importance of initiatives to include diverse populations into genomics research.