Next generation disparities in human genomics: concerns and remedies

Understanding Barriers to Engagement With a Prostate Cancer Research and Genetic Risk Service Among UK Men of Black African or Black Caribbean Ancestry

INTRODUCTION

Prostate cancer is the second most common cancer worldwide, and there is no national prostate cancer screening programme in the United Kingdom. Men of African ancestry are twice as likely to be diagnosed as men of European ancestry and are diagnosed at a younger age. Despite this, Black men are under-represented in seeking advice about prostate cancer symptoms, screening and genetic research. There is increasing research focused on targeted prostate cancer screening, using genetic testing to guide screening by identifying those at highest risk, but this could only be considered if people of all ethnicities would accept this approach. It is vital to diagnose prostate cancer early, when it is curable. We wanted to identify the barriers to engagement with prostate cancer genetic research to increase participation from those at highest risk.

METHODS

We conducted two community discussion groups, each attended by 30-35 Black men and their families. We conducted interviews with three Black community champions who have a lived experience of prostate cancer. Thematic analysis was performed on the transcripts. We used a participatory approach to develop our themes with members of the community, two of whom are co-authors on this paper.

RESULTS

Themes were grouped as barriers or facilitators to engagement with prostate cancer genetic risk services. Barriers included GP reluctance to perform prostate-specific antigen (PSA) testing, cultural inhibition around discussing prostate cancer and family history, fear of rectal examination, fear of cancer diagnosis and lack of trust in the healthcare system, no awareness about the role of genetics in prostate cancer risk assessment, negative connotations of genetic testing (e.g., genetic modification) and genetic data being used inappropriately. Facilitators were family and community support, the sharing of experiences, good communication with doctors, raised prostate cancer awareness, genetic risk assessment to guide the need for screening and facilitate early diagnosis, improving future outcomes for prostate cancer in the Black community through engaging with genetic research and assurance that there are regulations in place to protect genetic and personal data with guidance around when genetic results must be disclosed.

CONCLUSIONS

Understanding barriers and facilitators can guide recommendations for health services to improve access and uptake within the Black community and improve representation in genetic research. Better representation will support improvements in cancer outcomes and understanding of the genetic risk of prostate cancer in the Black community.

PATIENT OR PUBLIC CONTRIBUTION

We initially attended community prostate cancer awareness events to speak to members of the community. We established trusted and two-way relationships with Black 'community champions' who lead support groups in the Black community and often have a lived experience of prostate cancer. We were invited to attend their support groups to deliver awareness talks and address concerns about prostate cancer risk and screening. We then conducted discussion groups and collected data. Our analysis was conducted in partnership with our community champions. Our findings are described in this paper, with their co-authorship. We have also disseminated our findings in a co-produced newsletter to feed back our findings to the community members, who gave us their time. We have also shared information at a stakeholder day, attended by 65 individuals from the community, where we also planned future work. We have reimbursed participants for their time, which is in line with NIHR guidance. As described above, patient and public involvement has been the guiding principle throughout this project.

Susceptibility genes for allergic conjunctivitis revealed by cross-tissue transcriptome-wide association study

Allergic conjunctivitis (AC) is a common immune-mediated ocular disorder, characterized by clinical manifestations such as ocular itching, conjunctival hyperemia, lacrimation, and mucoid discharge, which significantly impair patients' visual function and quality of life. Despite extensive research efforts devoted to uncovering the genetic predisposition to AC, the underlying pathogenic genes and molecular mechanisms remain incompletely understood, necessitating further research to elucidate its genetic basis. This study utilized AC data from the FinnGen R12 and incorporated expression quantitative trait loci data in the Genotype-Tissue Expression v8 database to perform a cross-tissue transcriptome-wide association study (TWAS). Analytical methods included functional summary-based imputation (FUSION), unified test for molecular signatures (UTMOST), and gene analysis combined with multi-marker genome annotation (MAGMA). To further validate the results, Mendelian randomization (MR) analysis and colocalization analysis were performed. Through TWAS and MAGMA analyses, 13 susceptibility genes associated with AC were identified. Following MR and colocalization analyses, three candidate genes-GAL3ST2, PDCD1 and TLR6-were ultimately selected and validated by FUMA tool, which may influence progression of AC by regulating pathways related to Toll-like receptor signaling. In conclusion, three susceptibility genes linked to the risk of AC were identified, providing new insights into the genetic mechanisms and potential pathogenic pathways underlying AC.

Methodological opportunities in genomic data analysis to advance health equity

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

Universal Genetic Counseling and Testing for Black Women: A Risk-Stratified Approach to Addressing Breast Cancer Disparities

Black women experience disproportionate breast cancer-related mortality, with similar overall incidence to White women. Approaches to address these racial health disparities should be multifaceted. Universal genetic counseling and testing for Black women could represent one dimension of a comprehensive approach in guiding early identification of those more likely to experience higher breast cancer-related mortality. The increased risk of triple-negative breast cancer and greater likelihood of early-onset breast cancer among Black women are 2 major justifications, given that these elements are already preexisting testing criteria per the National Comprehensive Cancer Network. Increasing assessment of breast cancer-related risk in the Black community through universal genetic counseling and testing should be considered to focus enhanced screening and preventive measures in a tailored risk-appropriate context.

Revolutionising Cardio-Oncology Care with Precision Genomics

Cardiovascular disease is the worldwide leading cause of mortality among survivors of cancer due in part to the cardiotoxicity of anticancer therapies. This paper explores the progress in precision cardio-oncology, particularly in genetic testing and therapeutics, and its impact on cardiovascular diseases in clinical and laboratory settings. These advancements enable clinicians to better assess risk, diagnose conditions, and deliver personalised, cost-effective therapeutics. Through case studies of cancer-therapy-related cardiac dysfunction, clonal haematopoiesis of indeterminate potential, and polygenic risk scoring, we demonstrate the benefits of incorporating precision genomics in individualised care in cardio-oncology. Furthermore, leveraging real-world genomic data in clinical settings can advance our understanding of long noncoding RNAs and microRNAs, which play important regulatory roles in cardio-oncology. Additionally, employing human-induced pluripotent stem cells to stratify risk and guide prevention strategies represents a promising avenue for modelling precision cardio-oncology. While these advancements showcase the significant progress in genetic approaches, they also raise substantial ethical, legal, and societal concerns. Regulatory oversight of genetic and genomic technologies should therefore evolve suitably to keep up with rapid advancements in technology and analysis. Provider education is crucial for the appropriate use of new genetic and genomic applications, including on the existing protection available for patients regarding genetic information. This can provide confidence for diverse study groups to advance genetic studies looking to develop a comprehensive understanding and effective clinical applications for heterogeneous populations. In clinical settings, the implementation of genetic and genomic applications within electronic medical records can offer point-of-care clinical decision support, thus providing timely information to guide clinical management decisions.

Reclassification of variants of uncertain significance by race, ethnicity, and ancestry for patients at risk for breast cancer

Introduction

Although most variants of uncertain significance (VUS) in breast cancer susceptibility genes are eventually downgraded to benign or likely benign in individuals of European ancestry, it is unclear if this also applies to non-European populations. This study examines the time to and type of VUS reclassification among a diverse cohort at risk for breast cancer.

Methods

A multicenter retrospective analysis examined people assigned female at birth (AFAB) who underwent genetic testing from 2013 to 2021 with VUS in ATM, BARD1, BRCA1/2, CDH1, CHEK2, NF1, PALB2, PTEN, RAD51C/D, STK11, and/or TP53. Demographic data were collected [including race, ethnicity, and ancestry (REA)], as well as time to and type of reclassification. Frequency data and univariable and multivariable analyses were performed (p < 0.05 was considered statistically significant).

Results

There were 932 participants who had a total of 1,032 VUS (905 unique variants), with 20% who underwent reclassification of their results. The proportion of reclassified VUS among the largest represented REA groups was 19%, 23%, and 27% for White, Black or African American, and Asian people, respectively. REA was not associated with VUS reclassification (p = 0.25). The mean time to VUS reclassification was 2.8 years and was not significantly associated with REA (p = 0.16). Most VUS were downgraded to benign/likely benign (n = 187, 92%).

Discussion

Our findings demonstrate that REA is not significantly associated with VUS reclassification or time to reclassification, with the majority of VUS being downgraded across REA. This study allows for improved and more equitable genetic counseling. It may also provide more reassurance to those groups that may have a higher likelihood of VUS results.

Pharmacogenetics of childhood uncontrolled asthma

INTRODUCTION

Asthma is a heterogeneous, multifactorial disease with multiple genetic and environmental risk factors playing a role in pathogenesis and therapeutic response. Understanding of pharmacogenetics can help with matching individualized treatments to specific genotypes of asthma to improve therapeutic outcomes especially in uncontrolled or severe asthma.

AREAS COVERED

In this review, we outline novel information about biology, pathways, and mechanisms related to interindividual variability in drug response (corticosteroids, bronchodilators, leukotriene modifiers, and biologics) for childhood asthma. We discuss candidate gene, genome-wide association studies and newer omics studies including epigenomics, transcriptomics, proteomics, and metabolomics as well as integrative genomics and systems biology methods related to childhood asthma. The articles were obtained after a series of searches, last updated November 2022, using database PubMed/CINAHL DB.

EXPERT OPINION

Implementation of pharmacogenetic algorithms can improve therapeutic targeting in children with asthma, particularly with severe or uncontrolled asthma who typically have challenges in clinical management and carry considerable financial burden. Future studies focusing on potential biomarkers both clinical and pharmacogenetic can help formulate a prognostic test for asthma treatment response that would represent true bench to bedside clinical implementation.

IL-1 signaling enrichment in inflammatory skin disease loci with higher-risk allele frequencies in African ancestry

Inflammatory skin diseases (ISDs) exhibit varying prevalence across different ancestry background and geographical regions. Genetic research for complex ISDs has predominantly centered on European Ancestry (EurA) populations and genetic effects on immune cell responses but generally failed to consider contributions from other cell types in skin. Here, we utilized 273 genetic signals from seven different ISDs: acne, alopecia areata (AA), atopic dermatitis (AD), psoriasis, systemic lupus erythematosus (SLE), systemic sclerosis (SSc), and vitiligo, to demonstrate enriched IL1 signaling in keratinocytes, particularly in signals with higher risk allele frequencies in the African ancestry. Using a combination of ATAC-seq, Bru-seq, and promoter capture Hi-C, we revealed potential regulatory mechanisms of the acne locus on chromosome 2q13. We further demonstrated differential responses in keratinocytes upon IL1β stimulation, including the pro-inflammatory mediators CCL5, IL36G, and CXCL8. Taken together, our findings highlight IL1 signaling in epidermal keratinocytes as a contributor to ancestry-related differences in ISDs.

Bridging genomics' greatest challenge: The diversity gap

Achieving diverse representation in biomedical data is critical for healthcare equity. Failure to do so perpetuates health disparities and exacerbates biases that may harm patients with underrepresented ancestral backgrounds. We present a quantitative assessment of representation in datasets used across human genomics, including genome-wide association studies (GWASs), pharmacogenomics, clinical trials, and direct-to-consumer (DTC) genetic testing. We suggest that relative proportions of ancestries represented in datasets, compared to the global census population, provide insufficient representation of global ancestral genetic diversity. Some populations have greater proportional representation in data relative to their population size and the genomic diversity present in their ancestral haplotypes. As insights from genomics become increasingly integrated into evidence-based medicine, strategic inclusion and effective mechanisms to ensure representation of global genomic diversity in datasets are imperative.

Genomic and physiological mechanisms of high-altitude adaptation in Ethiopian highlanders: a comparative perspective

High-altitude adaptation is a remarkable example of natural selection, yet the genomic and physiological adaptation mechanisms of Ethiopian highlanders remain poorly understood compared to their Andean and Tibetan counterparts. Ethiopian populations, such as the Amhara and Oromo, exhibit unique adaptive strategies characterized by moderate hemoglobin levels and enhanced arterial oxygen saturation, indicating distinct mechanisms of coping with chronic hypoxia. This review synthesizes current genomic insights into Ethiopian high-altitude adaptation, identifying key candidate genes involved in hypoxia tolerance and examining the influence of genetic diversity and historical admixture on adaptive responses. Furthermore, the review highlights significant research gaps, particularly the underrepresentation of Ethiopian populations in global genomic studies, the lack of comprehensive genotype-phenotype analyses, and inconsistencies in research methodologies. Addressing these gaps is crucial for advancing our understanding of the genetic basis of human adaptation to extreme environments and for developing a more complete picture of human physiological resilience. This review offers a comparative perspective with Tibetan and Andean highlanders, emphasizing the need for expanding genomic representation and refining methodologies to uncover the genetic mechanisms underlying high-altitude adaptation in Ethiopian populations.

Eleven Grand Challenges for Inflammatory Bowel Disease Genetics and Genomics

The past 2 decades have witnessed extraordinary advances in our understanding of the genetic factors influencing inflammatory bowel disease (IBD), providing a foundation for the approaching era of genomic medicine. On behalf of the NIDDK IBD Genetics Consortium, we herein survey 11 grand challenges for the field as it embarks on the next 2 decades of research utilizing integrative genomic and systems biology approaches. These involve elucidation of the genetic architecture of IBD (how it compares across populations, the role of rare variants, and prospects of polygenic risk scores), in-depth cellular and molecular characterization (fine-mapping causal variants, cellular contributions to pathology, molecular pathways, interactions with environmental exposures, and advanced organoid models), and applications in personalized medicine (unmet medical needs, working toward molecular nosology, and precision therapeutics). We review recent advances in each of the 11 areas and pose challenges for the genetics and genomics communities of IBD researchers.

Polygene by environment interactions predicting depressive outcomes

Depression is a major public health problem with a continued need to uncover its etiology. Current models of depression contend that gene-by-environment (G × E) interactions influence depression risk, and further, that depression is polygenic. Thus, recent models have emphasized two polygenic approaches: a hypothesis-driven multilocus genetic profile score (MGPS; "MGPS × E") and a polygenic risk score (PRS; "PRS × E") derived from genome-wide association studies (GWAS). This review for the first time synthesizes current knowledge on polygene by environment "P × E" interaction research predicting primarily depression-related outcomes, and in brief, neurobiological outcomes. The "environment" of focus in this project is stressful life events. It further discusses findings in the context of differential susceptibility and diathesis-stress theories-two major theories guiding G × E work. This synthesis indicates that, within the MGPS literature, polygenic scores based on the serotonin system, the HPA axis, or across multiple systems, interact with environmental stress exposure to predict outcomes at multiple levels of analyses and most consistently align with differential susceptibility theory. Depressive outcomes are the most studied, but neuroendocrine, and neuroimaging findings are observed as well. By contrast, vast methodological differences between GWAS-based PRS studies contribute to mixed findings that yield inconclusive results.

Toll-like receptor agonists as cancer vaccine adjuvants

Cancer immunotherapy has emerged as a promising strategy to treat cancer patients. Among the wide range of immunological approaches, cancer vaccines have been investigated to activate and expand tumor-reactive T cells. However, most cancer vaccines have not shown significant clinical benefit as monotherapies. This is likely due to the antigen targets of vaccines, "self" proteins to which there is tolerance, as well as to the immunosuppressive tumor microenvironment. To help circumvent immune tolerance and generate effective immune responses, adjuvants for cancer vaccines are necessary. One representative adjuvant family is Toll-Like receptor (TLR) agonists, synthetic molecules that stimulate TLRs. TLRs are the largest family of pattern recognition receptors (PRRs) that serve as the sensors of pathogens or cellular damage. They recognize conserved foreign molecules from pathogens or internal molecules from cellular damage and propel innate immune responses. When used with vaccines, activation of TLRs signals an innate damage response that can facilitate the development of a strong adaptive immune response against the target antigen. The ability of TLR agonists to modulate innate immune responses has positioned them to serve as adjuvants for vaccines targeting infectious diseases and cancers. This review provides a summary of various TLRs, including their expression patterns, their functions in the immune system, as well as their ligands and synthetic molecules developed as TLR agonists. In addition, it presents a comprehensive overview of recent strategies employing different TLR agonists as adjuvants in cancer vaccine development, both in pre-clinical models and ongoing clinical trials.

Genomic insights for personalised care in lung cancer and smoking cessation: motivating at-risk individuals toward evidence-based health practices

BACKGROUND

Lung cancer and tobacco use pose significant global health challenges, necessitating a comprehensive translational roadmap for improved prevention strategies such as cancer screening and tobacco treatment, which are currently under-utilised. Polygenic risk scores (PRSs) may further motivate health behaviour change in primary care for lung cancer in diverse populations. In this work, we introduce the GREAT care paradigm, which integrates PRSs within comprehensive patient risk profiles to motivate positive health behaviour changes.

METHODS

We developed PRSs using large-scale multi-ancestry genome-wide association studies and standardised PRS distributions across all ancestries. We validated our PRSs in 561,776 individuals of diverse ancestry from the GISC Trial, UK Biobank (UKBB), and All of Us Research Program (AoU).

FINDINGS

Significant odds ratios (ORs) for lung cancer and difficulty quitting smoking were observed in both UKBB and AoU. For lung cancer, the ORs for individuals in the highest risk group (top 20% versus bottom 20%) were 1.85 (95% CI: 1.58-2.18) in UKBB and 2.39 (95% CI: 1.93-2.97) in AoU. For difficulty quitting smoking, the ORs (top 33% versus bottom 33%) were 1.36 (95% CI: 1.32-1.41) in UKBB and 1.32 (95% CI: 1.28-1.36) in AoU.

INTERPRETATION

Our PRS-based intervention model leverages large-scale genetic data for robust risk assessment across populations, which will be evaluated in two cluster-randomised clinical trials. This approach integrates genomic insights into primary care, promising improved outcomes in cancer prevention and tobacco treatment.

FUNDING

National Institutes of Health, NIH Intramural Research Program, National Science Foundation.

Adjusting for principal components can induce collider bias in genome-wide association studies

Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA. However, these suggestions are not universally implemented and the implications for GWAS are not fully understood, especially in the context of admixed populations. In this paper, we investigate the impact of pre-processing and the number of PCs included in GWAS models in African American samples from the Women's Health Initiative SNP Health Association Resource and two Trans-Omics for Precision Medicine Whole Genome Sequencing Project contributing studies (Jackson Heart Study and Genetic Epidemiology of Chronic Obstructive Pulmonary Disease Study). In all three samples, we find the first PC is highly correlated with genome-wide ancestry whereas later PCs often capture local genomic features. The pattern of which, and how many, genetic variants are highly correlated with individual PCs differs from what has been observed in prior studies focused on European populations and leads to distinct downstream consequences: adjusting for such PCs yields biased effect size estimates and elevated rates of spurious associations due to the phenomenon of collider bias. Excluding high LD regions identified in previous studies does not resolve these issues. LD pruning proves more effective, but the optimal choice of thresholds varies across datasets. Altogether, our work highlights unique issues that arise when using PCA to control for ancestral heterogeneity in admixed populations and demonstrates the importance of careful pre-processing and diagnostics to ensure that PCs capturing multiple local genomic features are not included in GWAS models.

Psychiatric neuroimaging at a crossroads: Insights from psychiatric genetics

Thanks to methodological advances, large-scale data collections, and longitudinal designs, psychiatric neuroimaging is better equipped than ever to identify the neurobiological underpinnings of youth mental health problems. However, the complexity of such endeavors has become increasingly evident, as the field has been confronted by limited clinical relevance, inconsistent results, and small effect sizes. Some of these challenges parallel those historically encountered by psychiatric genetics. In past genetic research, robust findings were historically undermined by oversimplified biological hypotheses, mistaken assumptions about expectable effect sizes, replication problems, confounding by population structure, and shared biological patterns across disorders. Overcoming these challenges has contributed to current successes in the field. Drawing parallels across psychiatric genetics and neuroimaging, we identify key shared challenges as well as pinpoint relevant insights that could be gained in psychiatric neuroimaging from the transition that occurred from the candidate gene to (post) genome-wide "eras" of psychiatric genetics. Finally, we discuss the prominent developmental component of psychiatric neuroimaging and how that might be informed by epidemiological and omics approaches. The evolution of psychiatric genetic research offers valuable insights that may expedite the resolution of key challenges in psychiatric neuroimaging, thus potentially moving our understanding of psychiatric pathophysiology forward.

Disparities in ABO blood type determination across diverse ancestries: a systematic review and validation in the All of Us Research Program

OBJECTIVES

ABO blood types have widespread clinical use and robust associations with disease. The purpose of this study is to evaluate the portability and suitability of tag single-nucleotide polymorphisms (tSNPs) used to determine ABO alleles and blood types across diverse populations in published literature.

MATERIALS AND METHODS

Bibliographic databases were searched for studies using tSNPs to determine ABO alleles. We calculated linkage between tSNPs and functional variants across inferred continental ancestry groups from 1000 Genomes. We compared r2 across ancestry and assessed real-world consequences by comparing tSNP-derived blood types to serology in a diverse population from the All of Us Research Program.

RESULTS

Linkage between functional variants and O allele tSNPs was significantly lower in African (median r2 = 0.443) compared to East Asian (r2 = 0.946, P = 1.1 × 10-5) and European (r2 = 0.869, P = .023) populations. In All of Us, discordance between tSNP-derived blood types and serology was high across all SNPs in African ancestry individuals and linkage was strongly correlated with discordance across all ancestries (ρ = -0.90, P = 3.08 × 10-23).

DISCUSSION

Many studies determine ABO blood types using tSNPs. However, tSNPs with low linkage disequilibrium promote misinference of ABO blood types, particularly in diverse populations. We observe common use of inappropriate tSNPs to determine ABO blood type, particularly for O alleles and with some tSNPs mistyping up to 58% of individuals.

CONCLUSION

Our results highlight the lack of transferability of tSNPs across ancestries and potential exacerbation of disparities in genomic research for underrepresented populations. This is especially relevant as more diverse cohorts are made publicly available.

Culturally responsive strategies and practical considerations for live tissue studies in Māori participant cohorts

Introduction

Indigenous communities globally are inequitably affected by non-communicable diseases such as cancer and coronary artery disease. Increased focus on personalized medicine approaches for the treatment of these diseases offers opportunities to improve the health of Indigenous people. Conversely, poorly implemented approaches pose increased risk of further exacerbating current inequities in health outcomes for Indigenous peoples. The advancement of modern biology techniques, such as three-dimensional (3D) in vitro models and next generation sequencing (NGS) technologies, have enhanced our understanding of disease mechanisms and individualized treatment responses. However, current representation of Indigenous peoples in these datasets is lacking. It is crucial that there is appropriate and ethical representation of Indigenous peoples in generated datasets to ensure these technologies can be used to maximize the benefit of personalized medicine for Indigenous peoples.

Methods

This project discusses the use of 3D tumor organoids and single cell/nucleus RNA sequencing to study cancer treatment responses and explore immune cell roles in coronary artery disease. Using key pillars from currently available Indigenous bioethics frameworks, strategies were developed for the use of Māori participant samples for live tissue and sequencing studies. These were based on extensive collaborations with local Māori community, scientific leaders, clinical experts, and international collaborators from the Broad Institute of MIT and Harvard. Issues surrounding the use of live tissue, genomic data, sending samples overseas and Indigenous data sovereignty were discussed.

Results

This paper illustrates a real-world example of how collaboration with community and the incorporation of Indigenous worldviews can be applied to molecular biology studies in a practical and culturally responsive manner, ensuring fair and equitable representation of Indigenous peoples in modern scientific data.

Fc gamma receptors: Their evolution, genomic architecture, genetic variation, and impact on human disease

Fc gamma receptors (FcγRs) are a family of receptors that bind IgG antibodies and interface at the junction of humoral and innate immunity. Precise regulation of receptor expression provides the necessary balance to achieve healthy immune homeostasis by establishing an appropriate immune threshold to limit autoimmunity but respond effectively to infection. The underlying genetics of the FCGR gene family are central to achieving this immune threshold by regulating affinity for IgG, signaling efficacy, and receptor expression. The FCGR gene locus was duplicated during evolution, retaining very high homology and resulting in a genomic region that is technically difficult to study. Here, we review the recent evolution of the gene family in mammals, its complexity and variation through copy number variation and single-nucleotide polymorphism, and impact of these on disease incidence, resolution, and therapeutic antibody efficacy. We also discuss the progress and limitations of current approaches to study the region and emphasize how new genomics technologies will likely resolve much of the current confusion in the field. This will lead to definitive conclusions on the impact of genetic variation within the FCGR gene locus on immune function and disease.

Predicting age of onset and progression of disease in late-onset genetic neurodegenerative diseases: An ethics review and research agenda

Currently, a prognostic biomarker-based model is being developed to predict the onset and disease progression of Huntington's Disease (HD) and Spinocerebellar Ataxia (SCA) types 1 and 3, both late-onset genetic neurodegenerative diseases lacking a disease-modifying treatment (DMT). The need for more accurate predictions of onset and disease progression arises in the context of clinical trials evaluating the effectiveness of potential DMTs and identifying the optimal time to initiate such a DMT. Moreover, such a prognostic model may provide mutation carriers with personal utility. The aim of this article is to anticipate the ethical issues raised by these new prognostic models and to formulate the ethical issues that need to be addressed to facilitate an ethically responsible development and implementation of such a model. Part one of this article describes the ethical issues raised by presymptomatic genetic testing for HD and evaluates whether and how these issues may also occur by predicting onset and disease progression. Part two presents the results of a critical interpretative review into the ethical issues raised by biomarker testing in other late-onset neurodegenerative diseases lacking a DMT. The review aims to identify new ethical issues related to biomarker testing for predicting the onset and disease progression of HD and SCA. Finally, based on parts one and two, part three proposes a research agenda for the near future regarding the most pressing ethical issues that need to be addressed to ensure an ethically responsible implementation of such a prognostic model in both research settings and clinical practice.

The role of circulating cytokines in heart failure: a bidirectional, two-sample Mendelian randomization study

Background

Cytokines play a pivotal role in the progression of heart failure (HF) by modulating inflammatory responses, promoting vasoconstriction, and facilitating endothelial injury. However, it is now difficult to distinguish the causal relationship between HF and cytokines in observational studies. Mendelian randomization (MR) analyses of cytokines probably could enhance our comprehension to the underlying biological processes of HF.

Methods

This study was to explore the correlation between 41 cytokines with HF at the genetic level by MR analysis. We selected a HF dataset from the Heart Failure Molecular Epidemiology for Therapeutic Targets (HERMES) 2018 and a cytokine dataset from a meta-analysis of cytokine levels in Finns. Two-sample, bidirectional MR analyses were performed using Inverse Variance Weighted (IVW), Weighted Median and MR- egger, and the results were tested for heterogeneity and pleiotropy, followed by sensitivity analysis.

Results

Genetic prediction of high levels of circulating Macrophage inflammatory pro-tein-1β(MIP-1β) (P = 0.0389), Interferon gamma induced protein 10(IP-10) (P = 0.0029), and Regu-lated on activation, normal T cell expressed and secreted(RANTES) (P = 0.0120) expression was associated with an elevated risk of HF. HF was associated with the increased levels of circulating Interleukin-2 receptor, alpha subunit(IL-2ra) (P = 0.0296), Beta nerve growth fac-tor(β-NGF) (P = 0.0446), Interleukin-17(IL-17) (P = 0.0360), Basic fibroblast growth factor(FGF-basic) (P = 0.0220), Platelet derived growth factor BB(PDGF-BB) (P = 0.0466), and Interferon-gamma(IFN-γ) (P = 0.0222); and with decreased levels of Eotaxin (P = 0.0133). The heterogeneity and pleiotropy of the cytokines were acceptable, except for minor heterogeneity of FGF-basic and IL-17.

Conclusion

These findings provide compelling evidence for a genetically predictive relationship between cytokines and HF, emphasizing a great potential of targeted modulation of cytokines in slowing the progression of HF. This study draws further conclusions at the genetic level, providing a basis for future large-scale clinical trials.

Rare coding variant analysis for human diseases across biobanks and ancestries

Large-scale sequencing has enabled unparalleled opportunities to investigate the role of rare coding variation in human phenotypic variability. Here, we present a pan-ancestry analysis of sequencing data from three large biobanks, including the All of Us research program. Using mixed-effects models, we performed gene-based rare variant testing for 601 diseases across 748,879 individuals, including 155,236 with ancestry dissimilar to European. We identified 363 significant associations, which highlighted core genes for the human disease phenome and identified potential novel associations, including UBR3 for cardiometabolic disease and YLPM1 for psychiatric disease. Pan-ancestry burden testing represented an inclusive and useful approach for discovery in diverse datasets, although we also highlight the importance of ancestry-specific sensitivity analyses in this setting. Finally, we found that effect sizes for rare protein-disrupting variants were concordant between samples similar to European ancestry and other genetic ancestries (βDeming = 0.7-1.0). Our results have implications for multi-ancestry and cross-biobank approaches in sequencing association studies for human disease.

ZMIX: estimating ancestry proportions using GWAS association Z-scores

Motivation

With larger and more diverse studies becoming the standard in genome-wide association studies (GWAS), accurate estimation of ancestral proportions is increasingly important for summary-statistics-based methods such as those for imputing association summary statistics, adjusting allele frequencies (AFs) for ancestry, and prioritizing disease candidate variants or genes. Existing methods for estimating ancestral proportions in GWAS rely on the availability of study reference AFs, which are often inaccessible in current GWAS due to privacy concerns.

Results

In this study, we propose ZMIX (Z-score-based estimation of ethnic MIXing proportions), a novel method for estimating ethnic mixing proportions in GWAS using only association Z-scores, and we compare its performance to existing reference AF-based methods in both real-world and simulated GWAS settings. We found that ZMIX offered comparable results to the reference AF-based methods in simulation and real-world studies. When applied to summary-statistics imputation, all three methods produced high-quality imputations with almost identical results.

Availability and implementation

https://github.com/statsleelab/gauss.

Genetic research within Indigenous communities: Engagement opportunities and pathways forward

PURPOSE

Against a historical backdrop of researchers who violated trust through lack of benefit sharing, transparency, and engagement, efforts are underway to develop better approaches for genetic and genomic research with Indigenous communities. To increase engagement, there is a need to understand factors that affect researcher and community collaborations. This study aimed to understand the barriers, challenges, and facilitators of Indigenous Peoples in the United States participating in genetic research.

METHODS

We conducted 42 semistructured interviews with Tribal leaders, clinicians, researchers, policy makers, and Tribal research review board members across the United States to explore perceived risks, benefits, barriers, and facilitators of genetic research participation.

RESULTS

Participants, identifying as Indigenous (88%) or non-Indigenous allies (12%), described their concerns, hesitancy, and fears about genetic research, as well as the roles of trust, transparency, and respect for culture in facilitating partnerships. Previous harms-such as sample and data misuse, stigmatization, or misrepresentation by researchers-revealed strategies for building trust to create more equitable and reciprocal research partnerships.

CONCLUSION

Participants in this study offered strategies for increasing genetic research engagement. The pathway forward should foster transparent research policies and practices to facilitate informed research that supports the needs and priorities of participants, communities, and researchers.

Genomic Insights for Personalized Care: Motivating At-Risk Individuals Toward Evidence-Based Health Practices

Background

Lung cancer and tobacco use pose significant global health challenges, necessitating a comprehensive translational roadmap for improved prevention strategies. Polygenic risk scores (PRSs) are powerful tools for patient risk stratification but have not yet been widely used in primary care for lung cancer, particularly in diverse patient populations.

Methods

We propose the GREAT care paradigm, which employs PRSs to stratify disease risk and personalize interventions. We developed PRSs using large-scale multi-ancestry genome-wide association studies and standardized PRS distributions across all ancestries. We applied our PRSs to 796 individuals from the GISC Trial, 350,154 from UK Biobank (UKBB), and 210,826 from All of Us Research Program (AoU), totaling 561,776 individuals of diverse ancestry.

Results

Significant odds ratios (ORs) for lung cancer and difficulty quitting smoking were observed in both UKBB and AoU. For lung cancer, the ORs for individuals in the highest risk group (top 20% versus bottom 20%) were 1.85 (95% CI: 1.58 - 2.18) in UKBB and 2.39 (95% CI: 1.93 - 2.97) in AoU. For difficulty quitting smoking, the ORs (top 33% versus bottom 33%) were 1.36 (95% CI: 1.32 - 1.41) in UKBB and 1.32 (95% CI: 1.28 - 1.36) in AoU.

Conclusion

Our PRS-based intervention model leverages large-scale genetic data for robust risk assessment across populations. This model will be evaluated in two cluster-randomized clinical trials aimed at motivating health behavior changes in high-risk patients of diverse ancestry. This pioneering approach integrates genomic insights into primary care, promising improved outcomes in cancer prevention and tobacco treatment.

Imputation accuracy across global human populations

Genotype imputation is now fundamental for genome-wide association studies but lacks fairness due to the underrepresentation of references from non-European ancestries. The state-of-the-art imputation reference panel released by the Trans-Omics for Precision Medicine (TOPMed) initiative improved the imputation of admixed African-ancestry and Hispanic/Latino samples, but imputation for populations primarily residing outside of North America may still fall short in performance due to persisting underrepresentation. To illustrate this point, we imputed the genotypes of over 43,000 individuals across 123 populations around the world and identified numerous populations where imputation accuracy paled in comparison to that of European-ancestry populations. For instance, the mean imputation r-squared (Rsq) for variants with minor allele frequencies between 1% and 5% in Saudi Arabians (n = 1,061), Vietnamese (n = 1,264), Thai (n = 2,435), and Papua New Guineans (n = 776) were 0.79, 0.78, 0.76, and 0.62, respectively, compared to 0.90-0.93 for comparable European populations matched in sample size and SNP array content. Outside of Africa and Latin America, Rsq appeared to decrease as genetic distances to European-ancestry reference increased, as predicted. Using sequencing data as ground truth, we also showed that Rsq may over-estimate imputation accuracy for non-European populations more than European populations, suggesting further disparity in accuracy between populations. Using 1,496 sequenced individuals from Taiwan Biobank as a second reference panel to TOPMed, we also assessed a strategy to improve imputation for non-European populations with meta-imputation, but this design did not improve accuracy across frequency spectra. Taken together, our analyses suggest that we must ultimately strive to increase diversity and size to promote equity within genetics research.

Ancestry-specific high-risk gene variant profiling unmasks diabetes-associated genes

How ancestry-associated genetic variance affects disparities in the risk of polygenic diseases and influences the identification of disease-associated genes warrants a deeper understanding. We hypothesized that the discovery of genes associated with polygenic diseases may be limited by the overreliance on single-nucleotide polymorphism (SNP)-based genomic investigation, as most significant variants identified in genome-wide SNP association studies map to introns and intergenic regions of the genome. To overcome such potential limitations, we developed a gene-constrained, function-based analytical method centered on high-risk variants (hrV) that encode frameshifts, stopgains or splice site disruption. We analyzed the total number of hrV per gene in populations of different ancestry, representing a total of 185 934 subjects. Using this analysis, we developed a quantitative index of hrV (hrVI) across 20 428 genes within each population. We then applied hrVI analysis to the discovery of genes associated with type 2 diabetes mellitus (T2DM), a polygenic disease with ancestry-related disparity. HrVI profiling and gene-to-gene comparisons of ancestry-specific hrV between the case (20 781 subjects) and control (24 440 subjects) populations in the T2DM national repository identified 57 genes associated with T2DM, 40 of which were discoverable only by ancestry-specific analysis. These results illustrate how a function-based, ancestry-specific analysis of genetic variations can accelerate the identification of genes associated with polygenic diseases. Besides T2DM, such analysis may facilitate our understanding of the genetic basis for other polygenic diseases that are also greatly influenced by environmental and behavioral factors, such as obesity, hypertension and Alzheimer's disease.

Adjusting for principal components can induce spurious associations in genome-wide association studies in admixed populations

Principal component analysis (PCA) is widely used to control for population structure in genome-wide association studies (GWAS). Top principal components (PCs) typically reflect population structure, but challenges arise in deciding how many PCs are needed and ensuring that PCs do not capture other artifacts such as regions with atypical linkage disequilibrium (LD). In response to the latter, many groups suggest performing LD pruning or excluding known high LD regions prior to PCA. However, these suggestions are not universally implemented and the implications for GWAS are not fully understood, especially in the context of admixed populations. In this paper, we investigate the impact of pre-processing and the number of PCs included in GWAS models in African American samples from the Women's Women's Health Initiative SNP Health Association Resource and two Trans-Omics for Precision Medicine Whole Genome Sequencing Project contributing studies (Jackson Heart Study and Genetic Epidemiology of Chronic Obstructive Pulmonary Disease Study). In all three samples, we find the first PC is highly correlated with genome-wide ancestry whereas later PCs often capture local genomic features. The pattern of which, and how many, genetic variants are highly correlated with individual PCs differs from what has been observed in prior studies focused on European populations and leads to distinct downstream consequences: adjusting for such PCs yields biased effect size estimates and elevated rates of spurious associations due to the phenomenon of collider bias. Excluding high LD regions identified in previous studies does not resolve these issues. LD pruning proves more effective, but the optimal choice of thresholds varies across datasets. Altogether, our work highlights unique issues that arise when using PCA to control for ancestral heterogeneity in admixed populations and demonstrates the importance of careful pre-processing and diagnostics to ensure that PCs capturing multiple local genomic features are not included in GWAS models.

Race and Ethnicity in Cardiac Surgery: A Missed Opportunity?

BACKGROUND

Patients' race and/or ethnicity are increasingly being associated with differential surgical access and outcomes in cardiac surgery. However, deriving evidence-based conclusions that can inform surgical care has been difficult because of poor diversity in study populations and conflicting research methodology and findings. Using a fictional patient example, this review identifies areas of concern in research engagement, methodology, and analyses, as well as potential steps to improve race and ethnicity considerations in cardiac surgical research.

METHODS

A narrative literature review was performed using the PubMed/MEDLINE and Google Scholar databases, with a combination of cardiac surgery, race, ethnicity, and disparities keywords.

RESULTS

Less than half of the published cardiac surgery randomized control trials report the race and/or ethnicity of research participants. Racial and/or ethnic minorities make up <20% of most study populations and are significantly underrepresented relative to their proportions of the general population. Further, race and/or ethnicity of research participants is variably categorized based on ancestry, geographic regions, cultural similarities, or minority status. There is growing consideration of analyzing interrelated and confounding variables, such as socioeconomic status, geographic location, or hospital quality, to better elucidate racial and/or ethnic disparities; however, intersectionality considerations remain limited in cardiac surgery research.

CONCLUSIONS

Racial and/or ethnic disparities are increasingly being reported in research engagement, cardiac pathologies, and surgical outcomes. To promote equitable surgical care, tangible efforts are needed to recruit racially and/or ethnically minoritized patients to research studies, be transparent and consistent in their groupings, and elucidate the impact of their intersectional social identities.

The association of GNB5 with Alzheimer disease revealed by genomic analysis restricted to variants impacting gene function

Disease-associated variants identified from genome-wide association studies (GWASs) frequently map to non-coding areas of the genome such as introns and intergenic regions. An exclusive reliance on gene-agnostic methods of genomic investigation could limit the identification of relevant genes associated with polygenic diseases such as Alzheimer disease (AD). To overcome such potential restriction, we developed a gene-constrained analytical method that considers only moderate- and high-risk variants that affect gene coding sequences. We report here the application of this approach to publicly available datasets containing 181,388 individuals without and with AD and the resulting identification of 660 genes potentially linked to the higher AD prevalence among Africans/African Americans. By integration with transcriptome analysis of 23 brain regions from 2,728 AD case-control samples, we concentrated on nine genes that potentially enhance the risk of AD: AACS, GNB5, GNS, HIPK3, MED13, SHC2, SLC22A5, VPS35, and ZNF398. GNB5, the fifth member of the heterotrimeric G protein beta family encoding Gβ5, is primarily expressed in neurons and is essential for normal neuronal development in mouse brain. Homozygous or compound heterozygous loss of function of GNB5 in humans has previously been associated with a syndrome of developmental delay, cognitive impairment, and cardiac arrhythmia. In validation experiments, we confirmed that Gnb5 heterozygosity enhanced the formation of both amyloid plaques and neurofibrillary tangles in the brains of AD model mice. These results suggest that gene-constrained analysis can complement the power of GWASs in the identification of AD-associated genes and may be more broadly applicable to other polygenic diseases.

Assembly of novel sequences for Chinese domestic pigs reveals new genes and regulatory variants providing new insights into their diversity

There is an increasing understanding that a reference genome representing an individual cannot capture all the gene repertoire of a species. Here, we conduct a population-scale missing sequences detection of Chinese domestic pigs using whole-genome sequencing data from 534 individuals. We identify 132.41 Mb of sequences absent in the reference assembly, including eight novel genes. In particular, the breeds spread in Chinese high-altitude regions perform significantly different frequencies of new sequences in promoters than other breeds. Furthermore, we dissect the role of non-coding variants and identify a novel sequence inserted in the 3'UTR of the FMO3 gene, which may be associated with the intramuscular fat phenotype. This novel sequence could be a candidate marker for meat quality. Our study provides a comprehensive overview of the missing sequences in Chinese domestic pigs and indicates that this dataset is a valuable resource for understanding the diversity and biology of pigs.

Transcriptome-wide association studies associated with Crohn's disease: challenges and perspectives

Crohn's disease (CD) is regarded as a lifelong progressive disease affecting all segments of the intestinal tract and multiple organs. Based on genome-wide association studies (GWAS) and gene expression data, transcriptome-wide association studies (TWAS) can help identify susceptibility genes associated with pathogenesis and disease behavior. In this review, we overview seven reported TWASs of CD, summarize their study designs, and discuss the key methods and steps used in TWAS, which affect the prioritization of susceptibility genes. This article summarized the screening of tissue-specific susceptibility genes for CD, and discussed the reported potential pathological mechanisms of overlapping susceptibility genes related to CD in a certain tissue type. We observed that ileal lipid-related metabolism and colonic extracellular vesicles may be involved in the pathogenesis of CD by performing GO pathway enrichment analysis for susceptibility genes. We further pointed the low reproducibility of TWAS associated with CD and discussed the reasons for these issues, strategies for solving them. In the future, more TWAS are needed to be designed into large-scale, unified cohorts, unified analysis pipelines, and fully classified databases of expression trait loci.

Dissociation of Reliability, Heritability, and Predictivity in Coarse- and Fine-Scale Functional Connectomes during Development

The functional connectome supports information transmission through the brain at various spatial scales, from exchange between broad cortical regions to finer-scale, vertex-wise connections that underlie specific information processing mechanisms. In adults, while both the coarse- and fine-scale functional connectomes predict cognition, the fine scale can predict up to twice the variance as the coarse-scale functional connectome. Yet, past brain-wide association studies, particularly using large developmental samples, focus on the coarse connectome to understand the neural underpinnings of individual differences in cognition. Using a large cohort of children (age 9-10 years; n = 1,115 individuals; both sexes; 50% female, including 170 monozygotic and 219 dizygotic twin pairs and 337 unrelated individuals), we examine the reliability, heritability, and behavioral relevance of resting-state functional connectivity computed at different spatial scales. We use connectivity hyperalignment to improve access to reliable fine-scale (vertex-wise) connectivity information and compare the fine-scale connectome with the traditional parcel-wise (coarse scale) functional connectomes. Though individual differences in the fine-scale connectome are more reliable than those in the coarse-scale, they are less heritable. Further, the alignment and scale of connectomes influence their ability to predict behavior, whereby some cognitive traits are equally well predicted by both connectome scales, but other, less heritable cognitive traits are better predicted by the fine-scale connectome. Together, our findings suggest there are dissociable individual differences in information processing represented at different scales of the functional connectome which, in turn, have distinct implications for heritability and cognition.

Underrepresentation of Diverse Ancestries Drives Uncertainty in Genetic Variants Found in Cardiomyopathy-Associated Genes

BACKGROUND

Thousands of genetic variants have been identified in cardiomyopathy-associated genes. Diagnostic genetic testing is key for evaluation of individuals with suspected cardiomyopathy. While accurate variant pathogenicity assignment is important for diagnosis, the frequency of and factors associated with clinically relevant assessment changes are unclear.

OBJECTIVES

The authors aimed to characterize pathogenicity assignment change in cardiomyopathy-associated genes and to identify factors associated with this change.

METHODS

We identified 10 sarcomeric and 6 desmosomal genetic cardiomyopathy-associated genes along with comparison gene sets. We analyzed clinically meaningful changes in pathogenicity assignment between any of the following: pathogenic/likely pathogenic (P/LP), conflicting interpretations of pathogenicity or variant of unknown significance (C/VUS), and benign/likely benign. We explored association of minor allele frequency (MAF) differences between well, and traditionally poorly, represented ancestries in genetic studies with assessment stability. Analyses were performed using ClinVar and GnomAD data.

RESULTS

Of the 30,975 cardiomyopathy-associated gene variants in ClinVar, 2,276 of them (7.3%) had a clinically meaningful change in pathogenicity assignment over the study period, 2011 to 2021. Sixty-seven percent of variants that underwent a clinically significant change moved from P/LP or benign/likely benign to C/VUS. Among cardiomyopathy variants downgraded from P/LP, 35% had a MAF above 1 × 10 -4 in non-Europeans and below 1 × 10 -4 in Europeans.

CONCLUSIONS

Over the past 10 years, 7.3% of cardiomyopathy gene variants underwent a clinically meaningful change in pathogenicity assignment. Over 30% of downgrades from P/LP may be attributable to higher MAF in Non-Europeans than Europeans. This finding suggests that low ancestral diversity in genetic studies has increased diagnostic uncertainty in cardiomyopathy gene variants.

Addressing Ancestry and Sex Bias in Pharmacogenomics

The association of an individual's genetic makeup with their response to drugs is referred to as pharmacogenomics. By understanding the relationship between genetic variants and drug efficacy or toxicity, we are able to optimize pharmacological therapy according to an individual's genotype. Pharmacogenomics research has historically suffered from bias and underrepresentation of people from certain ancestry groups and of the female sex. These biases can arise from factors such as drugs and indications studied, selection of study participants, and methods used to collect and analyze data. To examine the representation of biogeographical populations in pharmacogenomic data sets, we describe individuals involved in gene-drug response studies from PharmGKB, a leading repository of drug-gene annotations, and showcaseCYP2D6, a gene that metabolizes approximately 25% of all prescribed drugs. We also show how the historical underrepresentation of females in clinical trials has led to significantly more adverse drug reactions in females than in males.

Oral microbiota, co-evolution, and implications for health and disease: The case of indigenous peoples

Evidence indicates that oral microbiota plays a crucial role in human health and disease. For instance, diseases with multifactorial etiology, such as periodontitis and caries, which cause a detrimental impact on human well-being and health, can be caused by alterations in the host-microbiota interactions, where non-pathogenic bacteria give way to pathogenic orange/red-complex bacterial species (a change from a eubiotic to dysbiotic state). In this scenario, where thousands of oral microorganisms, including fungi, archaea, and phage species, and their host are co-evolving, a set of phenomena, such as the arms race and Red or Black Queen dynamics, are expected to operate. We review concepts on the subject and revisit the nature of bacterial complexes linked to oral health and diseases, as well as the problem of the bacterial resistome in the face of the use of antibiotics and what is the impact of this on the evolutionary trajectory of the members of this symbiotic ecosystem. We constructed a 16SrRNA tree to show that adaptive consortia of oral bacterial complexes do not necessarily rescue phylogenetic relationships. Finally, we remember that oral health is not exempt from health disparity trends in some populations, such as Native Americans, when compared with non-Indigenous people.

Towards pharmacogenomics-guided tuberculosis (TB) therapy: N-acetyltransferase-2 genotypes among TB-infected Kenyans of mixed ethnicity

BACKGROUND

Though persons of African descent have one of the widest genetic variability, genetic polymorphisms of drug-metabolising enzymes such as N-Acetyltransferase-2 (NAT2) are understudied. This study aimed to identify prevalent NAT2 single nucleotide polymorphisms (SNPs) and infer their potential effects on enzyme function among Kenyan volunteers with tuberculosis (TB) infection. Genotypic distribution at each SNP and non-random association of alleles were evaluated by testing for Hardy-Weinberg Equilibrium (HWE) and Linkage Disequilibrium (LD).

METHODS

We isolated genomic DNA from cryopreserved Peripheral Blood Mononuclear Cells of 79 volunteers. We amplified the protein-coding region of the NAT2 gene by polymerase chain reaction (PCR) and sequenced PCR products using the Sanger sequencing method. Sequencing reads were mapped and aligned to the NAT2 reference using the Geneious software (Auckland, New Zealand). Statistical analyses were performed using RStudio version 4.3.2 (2023.09.1 + 494).

RESULTS

The most frequent haplotype was the wild type NAT2*4 (37%). Five genetic variants: 282C > T (NAT2*13), 341 T > C (NAT2*5), 803A > G (NAT2*12), 590G > A (NAT2*6) and 481C > T (NAT2*11) were observed with allele frequencies of 29%, 18%, 6%, 6%, and 4% respectively. According to the bimodal distribution of acetylation activity, the predicted phenotype was 76% rapid (mainly consisting of the wildtype NAT2*4 and the NAT2*13A variant). A higher proportion of rapid acetylators were female, 72% vs 28% male (p = 0.022, odds ratio [OR] 3.48, 95% confidence interval [CI] 1.21 to 10.48). All variants were in HWE. NAT2 341 T > C was in strong complete LD with the 590G > A variant (D' = 1.0, r2 = - 0.39) but not complete LD with the 282C > T variant (D' = 0.94, r2 = - 0.54).

CONCLUSION

The rapid acetylation haplotypes predominated. Despite the LD observed, none of the SNPs could be termed tag SNP. This study adds to the genetic characterisation data of African populations at NAT2, which may be useful for developing relevant pharmacogenomic tools for TB therapy. To support optimised, pharmacogenomics-guided TB therapy, we recommend genotype-phenotype studies, including studies designed to explore gender-associated differences.

Disparities in the Demographic Composition of The Cancer Imaging Archive

Purpose To characterize the demographic distribution of The Cancer Imaging Archive (TCIA) studies and compare them with those of the U.S. cancer population. Materials and Methods In this retrospective study, data from TCIA studies were examined for the inclusion of demographic information. Of 189 studies in TCIA up until April 2023, a total of 83 human cancer studies were found to contain supporting demographic data. The median patient age and the sex, race, and ethnicity proportions of each study were calculated and compared with those of the U.S. cancer population, provided by the Surveillance, Epidemiology, and End Results Program and the Centers for Disease Control and Prevention U.S. Cancer Statistics Data Visualizations Tool. Results The median age of TCIA patients was found to be 6.84 years lower than that of the U.S. cancer population (P = .047) and contained more female than male patients (53% vs 47%). American Indian and Alaska Native, Black or African American, and Hispanic patients were underrepresented in TCIA studies by 47.7%, 35.8%, and 14.7%, respectively, compared with the U.S. cancer population. Conclusion The results demonstrate that the patient demographics of TCIA data sets do not reflect those of the U.S. cancer population, which may decrease the generalizability of artificial intelligence radiology tools developed using these imaging data sets. Keywords: Ethics, Meta-Analysis, Health Disparities, Cancer Health Disparities, Machine Learning, Artificial Intelligence, Race, Ethnicity, Sex, Age, Bias Published under a CC BY 4.0 license.

Polygenic Risk Score Associates With Atherosclerotic Plaque Characteristics at Autopsy

BACKGROUND

Polygenic risk scores (PRSs) for coronary artery disease (CAD) potentially improve cardiovascular risk prediction. However, their relationship with histopathologic features of CAD has never been examined systematically.

METHODS

From 4327 subjects referred to CVPath by the State of Maryland Office Chief Medical Examiner for sudden death between 1994 and 2015, 2455 cases were randomly selected for genotyping. We generated PRS from 291 known CAD risk loci. Detailed histopathologic examination of the coronary arteries was performed in all subjects. The primary study outcome measurements were histopathologic plaque features determining severity of atherosclerosis, including %stenosis, calcification, thin-cap fibroatheromas, and thrombotic CAD.

RESULTS

After exclusion of cases with insufficient DNA sample quality or with missing data, 954 cases (mean age, 48.8±14.7 years; 75.7% men) remained in the final study cohort. Subjects in the highest PRS quintile exhibited more severe atherosclerosis compared with subjects in the lowest quintile, with greater %stenosis (80.3%±27.0% versus 50.4%±38.7%; adjusted P<0.001) and a higher frequency of calcification (69.6% versus 35.8%; adjusted P=0.004) and thin-cap fibroatheroma (26.7% versus 9.5%; adjusted P=0.007). Even after adjustment for traditional CAD risk factors, subjects within the highest PRS quintile had higher odds of severe atherosclerosis (ie, ≥75% stenosis; adjusted odds ratio, 3.77 [95% CI, 2.10-6.78]; P<0.001) and plaque rupture (adjusted odds ratio, 4.05 [95% CI, 2.26-7.24]; P<0.001). Moreover, subjects within the highest quintile had higher odds of CAD-associated cause of death, especially among those aged ≤50 years (adjusted odds ratio, 4.08 [95% CI, 2.01-8.30]; P<0.001). No statistically significant associations were observed with plaque erosion after adjusting for covariates.

CONCLUSIONS

This is the first autopsy study investigating associations between PRS and atherosclerosis severity at the histopathologic level in subjects with sudden death. Our pathological analysis suggests PRS correlates with plaque burden and features of advanced atherosclerosis and may be useful as a method for CAD risk stratification, especially in younger subjects.

Population diversity and equity in the genomic era: going global to return to the local

Advances in precision medicine depend on the quantity and quality of available genomic information. Various articles alert about the current disparities between the world's regions regarding the amount of genomic information available and the negative impact this will have on global health. The objective of this paper is to review these articles to describe what aspects they emphasize and highlight some issues that remain to be analyzed from the perspective of a "peripheral" country. Most of these articles come from central countries, where the need for more diversity in genomics is already detected. Several authors analyze lack of human diversity with focus on national, while others analyze the problem from a global perspective. Depending on the country of origin of the research, the claim for greater diversity has different meanings. Broadly, high-income countries advocate for better coverage looking within the boundaries of their own countries. In other regions of the world, where this field of research has not yet been massively developed, the same need for greater inclusiveness of origins in population genomics studies is not detected. An under-analyzed aspect is the unequal starting point between regions regarding the economic resources available for the development of this field of medicine, and for science and health in general. Although this macroeconomic and social aspect is usually absent in scientific analyses, without it solved, it will be impossible to guarantee that all world populations are equally represented in the panels or genomic databases that serve as input for precision medicine development.

Leveraging African American family connectors for Alzheimer's disease genomic studies

INTRODUCTION

The underrepresentation of African Americans (AAs) in Alzheimer's disease (AD) research may limit potential benefits from translational applications. This article describes an approach to recruit AA families into an AD genomic study and characteristics of seeds (family connectors) used to overcome recruitment barriers of AA families into AD research.

METHODS

A four-step outreach and snowball sampling approach relying on family connectors was used to recruit AA families. Descriptive statistics of a profile survey were gathered to understand the demographic and health characteristics of family connectors.

RESULTS

Twenty-five AA families (117 participants) were enrolled in the study via family connectors. Most family connectors self-identified as female (88%), were 60 years of age or older (76%), and attained post-secondary education (77%).

DISCUSSION

Community-engaged strategies were essential to recruit AA families. Relationships between study coordinators and family connectors build trust early in the research process among AA families.

HIGHLIGHTS

Community events were most effective for recruiting African American families. Family connectors were primarily female, in good health, and highly educated. Systematic efforts by researchers are necessary to "sell" a study to participants.

Imputation Accuracy Across Global Human Populations

Genotype imputation is now fundamental for genome-wide association studies but lacks fairness due to the underrepresentation of populations with non-European ancestries. The state-of-the-art imputation reference panel released by the Trans-Omics for Precision Medicine (TOPMed) initiative contains a substantial number of admixed African-ancestry and Hispanic/Latino samples to impute these populations with nearly the same accuracy as European-ancestry cohorts. However, imputation for populations primarily residing outside of North America may still fall short in performance due to persisting underrepresentation. To illustrate this point, we curated genome-wide array data from 23 publications published between 2008 to 2021. In total, we imputed over 43k individuals across 123 populations around the world. We identified a number of populations where imputation accuracy paled in comparison to that of European-ancestry populations. For instance, the mean imputation r-squared (Rsq) for 1-5% alleles in Saudi Arabians (N=1061), Vietnamese (N=1264), Thai (N=2435), and Papua New Guineans (N=776) were 0.79, 0.78, 0.76, and 0.62, respectively. In contrast, the mean Rsq ranged from 0.90 to 0.93 for comparable European populations matched in sample size and SNP content. Outside of Africa and Latin America, Rsq appeared to decrease as genetic distances to European reference increased, as predicted. Further analysis using sequencing data as ground truth suggested that imputation software may over-estimate imputation accuracy for non-European populations than European populations, suggesting further disparity between populations. Using 1496 whole genome sequenced individuals from Taiwan Biobank as a reference, we also assessed a strategy to improve imputation for non-European populations with meta-imputation, which can combine results from TOPMed with smaller population-specific reference panels. We found that meta-imputation in this design did not improve Rsq genome-wide. Taken together, our analysis suggests that with the current size of alternative reference panels, meta-imputation alone cannot improve imputation efficacy for underrepresented cohorts and we must ultimately strive to increase diversity and size to promote equity within genetics research.

Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations

Precision medicine models often perform better for populations of European ancestry due to the over-representation of this group in the genomic datasets and large-scale biobanks from which the models are constructed. As a result, prediction models may misrepresent or provide less accurate treatment recommendations for underrepresented populations, contributing to health disparities. This study introduces an adaptable machine learning toolkit that integrates multiple existing methodologies and novel techniques to enhance the prediction accuracy for underrepresented populations in genomic datasets. By leveraging machine learning techniques, including gradient boosting and automated methods, coupled with novel population-conditional re-sampling techniques, our method significantly improves the phenotypic prediction from single nucleotide polymorphism (SNP) data for diverse populations. We evaluate our approach using the UK Biobank, which is composed primarily of British individuals with European ancestry, and a minority representation of groups with Asian and African ancestry. Performance metrics demonstrate substantial improvements in phenotype prediction for underrepresented groups, achieving prediction accuracy comparable to that of the majority group. This approach represents a significant step towards improving prediction accuracy amidst current dataset diversity challenges. By integrating a tailored pipeline, our approach fosters more equitable validity and utility of statistical genetics methods, paving the way for more inclusive models and outcomes.

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.

The ethical challenges of diversifying genomic data: A qualitative evidence synthesis

This article aims to explore the ethical issues arising from attempts to diversify genomic data and include individuals from underserved groups in studies exploring the relationship between genomics and health. We employed a qualitative synthesis design, combining data from three sources: 1) a rapid review of empirical articles published between 2000 and 2022 with a primary or secondary focus on diversifying genomic data, or the inclusion of underserved groups and ethical issues arising from this, 2) an expert workshop and 3) a narrative review. Using these three sources we found that ethical issues are interconnected across structural factors and research practices. Structural issues include failing to engage with the politics of knowledge production, existing inequities, and their effects on how harms and benefits of genomics are distributed. Issues related to research practices include a lack of reflexivity, exploitative dynamics and the failure to prioritise meaningful co-production. Ethical issues arise from both the structure and the practice of research, which can inhibit researcher and participant opportunities to diversify data in an ethical way. Diverse data are not ethical in and of themselves, and without being attentive to the social, historical and political contexts that shape the lives of potential participants, endeavours to diversify genomic data run the risk of worsening existing inequities. Efforts to construct more representative genomic datasets need to develop ethical approaches that are situated within wider attempts to make the enterprise of genomics more equitable.

Population history modulates the fitness effects of Copy Number Variation in the Roma

We provide the first whole genome Copy Number Variant (CNV) study addressing Roma, along with reference populations from South Asia, the Middle East and Europe. Using CNV calling software for short-read sequence data, we identified 3171 deletions and 489 duplications. Taking into account the known population history of the Roma, as inferred from whole genome nucleotide variation, we could discern how this history has shaped CNV variation. As expected, patterns of deletion variation, but not duplication, in the Roma followed those obtained from single nucleotide polymorphisms (SNPs). Reduced effective population size resulting in slightly relaxed natural selection may explain our observation of an increase in intronic (but not exonic) deletions within Loss of Function (LoF)-intolerant genes. Over-representation analysis for LoF-intolerant gene sets hosting intronic deletions highlights a substantial accumulation of shared biological processes in Roma, intriguingly related to signaling, nervous system and development features, which may be related to the known profile of private disease in the population. Finally, we show the link between deletions and known trait-related SNPs reported in the genome-wide association study (GWAS) catalog, which exhibited even frequency distributions among the studied populations. This suggests that, in general human populations, the strong association between deletions and SNPs associated to biomedical conditions and traits could be widespread across continental populations, reflecting a common background of potentially disease/trait-related CNVs.

Guidelines for microbiome studies in renal physiology

Gut microbiome research has increased dramatically in the last decade, including in renal health and disease. The field is moving from experiments showing mere association to causation using both forward and reverse microbiome approaches, leveraging tools such as germ-free animals, treatment with antibiotics, and fecal microbiota transplantations. However, we are still seeing a gap between discovery and translation that needs to be addressed, so that patients can benefit from microbiome-based therapies. In this guideline paper, we discuss the key considerations that affect the gut microbiome of animals and clinical studies assessing renal function, many of which are often overlooked, resulting in false-positive results. For animal studies, these include suppliers, acclimatization, baseline microbiota and its normalization, littermates and cohort/cage effects, diet, sex differences, age, circadian differences, antibiotics and sweeteners, and models used. Clinical studies have some unique considerations, which include sampling, gut transit time, dietary records, medication, and renal phenotypes. We provide best-practice guidance on sampling, storage, DNA extraction, and methods for microbial DNA sequencing (both 16S rRNA and shotgun metagenome). Finally, we discuss follow-up analyses, including tools available, metrics, and their interpretation, and the key challenges ahead in the microbiome field. By standardizing study designs, methods, and reporting, we will accelerate the findings from discovery to translation and result in new microbiome-based therapies that may improve renal health.

Genetically predicted circulating levels of cytokines and the risk of depression: a bidirectional Mendelian-randomization study

Objective: Inflammatory cytokines disturbance is the main result of immune dysregulation, which is widely described in major depressive disorder (MDD). However, the potential causal relationship between these two factors has not been discovered. Therefore, the purpose of this study was to investigate the causal relationship between inflammatory cytokines and MDD risk by using the two-sample Mendelian randomization (MR) analysis. Method: Two genetic instruments obtained from publicly available gene profile data were utilized for the analysis. We obtained the genetic variation data of 41 inflammatory cytokines from genome-wide association studies (GWAS) meta-analysis of 8293 individuals of Finnish descent. The MDD data, including 135,458 MDD cases and 344,901 controls, were obtained from the Psychiatric Genomics Consortium Database. For the Mendelian randomization (MR) estimation, several methods were employed, namely, MR-Egger regression, inverse-variance weighted (IVW), weighted median, and MR-Pleiotropy RESidual Sum and Outlier (MR-PRESSO) methods. Result: A causal relationship was identified between the genetically proxied levels of Interleukin (IL) -18, IL-1β, and Regulated upon activation normal T cell expressed and secreted (RANTES) and the risk of MDD (OR = 0.968, 95%CI = 0.938, 0.998, p = 0.036; OR = 0.875, 95%CI = 0.787, 0.971, p = 0.012; OR = 0.947, 95%CI = 0.902, 0.995, p = 0.03; respectively). However, our Mendelian randomization (MR) estimates provided no causality of MDD on inflammatory cytokines. Conclusion: Our study elucidates the connection between inflammatory cytokines and MDD by using MR analysis, thereby enhancing our comprehension of the potential mechanisms. By identifying these associations, our findings hold substantial implications for the development of more effective treatments aimed at improving patient outcomes. However, further investigation is required to fully comprehend the exact biological mechanisms involved.

ClinVar and HGMD genomic variant classification accuracy has improved over time, as measured by implied disease burden

BACKGROUND

Curated databases of genetic variants assist clinicians and researchers in interpreting genetic variation. Yet, these databases contain some misclassified variants. It is unclear whether variant misclassification is abating as these databases rapidly grow and implement new guidelines.

METHODS

Using archives of ClinVar and HGMD, we investigated how variant misclassification has changed over 6 years, across different ancestry groups. We considered inborn errors of metabolism (IEMs) screened in newborns as a model system because these disorders are often highly penetrant with neonatal phenotypes. We used samples from the 1000 Genomes Project (1KGP) to identify individuals with genotypes that were classified by the databases as pathogenic. Due to the rarity of IEMs, nearly all such classified pathogenic genotypes indicate likely variant misclassification in ClinVar or HGMD.

RESULTS

While the false-positive rates of both ClinVar and HGMD have improved over time, HGMD variants currently imply two orders of magnitude more affected individuals in 1KGP than ClinVar variants. We observed that African ancestry individuals have a significantly increased chance of being incorrectly indicated to be affected by a screened IEM when HGMD variants are used. However, this bias affecting genomes of African ancestry was no longer significant once common variants were removed in accordance with recent variant classification guidelines. We discovered that ClinVar variants classified as Pathogenic or Likely Pathogenic are reclassified sixfold more often than DM or DM? variants in HGMD, which has likely resulted in ClinVar's lower false-positive rate.

CONCLUSIONS

Considering misclassified variants that have since been reclassified reveals our increasing understanding of rare genetic variation. We found that variant classification guidelines and allele frequency databases comprising genetically diverse samples are important factors in reclassification. We also discovered that ClinVar variants common in European and South Asian individuals were more likely to be reclassified to a lower confidence category, perhaps due to an increased chance of these variants being classified by multiple submitters. We discuss features for variant classification databases that would support their continued improvement.