Genomics is failing on diversity

Conceptual frameworks for the integration of genetic and social epidemiology in complex diseases

Uncovering the root causes of complex diseases requires complex approaches, yet many studies continue to isolate the effects of genetic and social determinants of disease. Epidemiologic efforts that under-utilize genetic epidemiology methods and findings may lead to incomplete understanding of disease. Meanwhile, genetic epidemiology studies are often conducted without consideration of social and environmental context, limiting the public health impact of genomic discoveries. This divide endures despite shared goals and increases in interdisciplinary data due to a lack of shared theoretical frameworks and differing language. Here, we demonstrate that bridging epidemiological divides does not require entirely new ways of thinking. Existing social epidemiology frameworks including Ecosocial theory and Fundamental Cause Theory, can both be extended to incorporate principles from genetic epidemiology. We show that genetic epidemiology can strengthen, rather than detract from, efforts to understand the impact of social determinants of health. In addition to presenting theoretical synergies, we offer practical examples of how genetics can improve the public health impact of epidemiology studies across the field. Ultimately, we aim to provide a guiding framework for trainees and established epidemiologists to think about diseases and complex systems and foster more fruitful collaboration between genetic and traditional epidemiological disciplines.

Seeing the random forest through the decision trees. Supporting learning health systems from histopathology with machine learning models: Challenges and opportunities

This paper discusses some overlooked challenges faced when working with machine learning models for histopathology and presents a novel opportunity to support "Learning Health Systems" with them. Initially, the authors elaborate on these challenges after separating them according to their mitigation strategies: those that need innovative approaches, time, or future technological capabilities and those that require a conceptual reappraisal from a critical perspective. Then, a novel opportunity to support "Learning Health Systems" by integrating hidden information extracted by ML models from digitalized histopathology slides with other healthcare big data is presented.

Massively parallel approaches for characterizing noncoding functional variation in human evolution

The genetic differences underlying unique phenotypes in humans compared to our closest primate relatives have long remained a mystery. Similarly, the genetic basis of adaptations between human groups during our expansion across the globe is poorly characterized. Uncovering the downstream phenotypic consequences of these genetic variants has been difficult, as a substantial portion lies in noncoding regions, such as cis-regulatory elements (CREs). Here, we review recent high-throughput approaches to measure the functions of CREs and the impact of variation within them. CRISPR screens can directly perturb CREs in the genome to understand downstream impacts on gene expression and phenotypes, while massively parallel reporter assays can decipher the regulatory impact of sequence variants. Machine learning has begun to be able to predict regulatory function from sequence alone, further scaling our ability to characterize genome function. Applying these tools across diverse phenotypes, model systems, and ancestries is beginning to revolutionize our understanding of noncoding variation underlying human evolution.

Attitudes towards disclosure of familial genetic risk in a Mediterranean island population - A survey of the Maltese population

Germline genetic testing has implications that extend beyond the individual patient to relatives, particularly for high-penetrance variants implicated in hereditary cancer or neurodegenerative syndromes. Many countries encourage patient-led communication to inform at-risk relatives, although the efficacy and uptake of this approach varies. Alternative scenarios envisage direct contact mediated by clinicians. The familial disclosure of sensitive genetic information is also determined by complex socio-ethnic factors. To date, no study has explored whether relatives would want to be informed of familial genetic risk and their preferences on different methods of communication in Malta. We thus used a published instrument that utilizes hypothetical scenario methodology to survey the attitudes of the Maltese population (n = 334) to receiving genetic information from family members. Two vignettes on Huntington's disease and colorectal cancer were presented. We also explored preferences towards the communication of genetic risk, confidentiality, and disclosure policies. Our preliminary results show that most respondents want to be informed of their increased risk by a family member or a clinician and would opt to receive confirmatory genetic testing. Most respondents preferred being informed of genetic risk by a close relative, but in the case of non-disclosure would want to be informed by a clinician. Most respondents expressed preference in favour of the introduction of registries, legislative change and sharing of contact details to address cases of nondisclosure. Our findings contribute further to evidence that supports, in selected hypothetical scenarios, an envisioned change in disclosure of genetic data policy by the public that is different from current practice to date.

Genomic Exploration of Essential Hypertension in African-Brazilian Quilombo Populations: A Comprehensive Approach with Pedigree Analysis and Family-Based Association Studies

BACKGROUND

Essential Hypertension (EH) is a global health issue, responsible for approximately 9.4 million deaths annually. Its prevalence varies by region, with genetic factors contributing 30-60% to blood pressure variation. Despite extensive research, the genetic complexity of EH remains largely unexplained. This study aimed to investigate the genetic basis of EH in African-derived individuals from partially isolated quilombo remnant populations in Vale do Ribeira (SP-Brazil).

METHODS

Samples from 431 individuals (167 affected, 261 unaffected, 3 with unknown phenotype) were genotyped using a 650k SNP array. Global ancestry proportions were estimated at 47% African, 36% European, and 16% Native American. Additional data from 673 individuals were used to construct six pedigrees. Pedigrees were pruned, and three non-overlapping marker subpanels were created. We phased haplotypes and performed local ancestry analysis to account for admixture. We then conducted genome-wide linkage analysis (GWLA) and performed fine-mapping through family-based association studies (FBAS) on imputed data and through EH-related genes investigation.

RESULTS

Linkage analysis identified 22 ROIs with LOD scores ranging from 1.45 to 3.03, encompassing 2363 genes. Fine-mapping identified 60 EH-related candidate genes and 118 suggestive or significant variants (FBAS). Among these, 14 genes, including PHGDH, S100A10, MFN2, and RYR2, were strongly associated with hypertension and harbors 29 SNPs.

CONCLUSIONS

Through a complementary approach - combining admixture-adjusted genome-wide linkage analysis based on Markov chain Monte Carlo (MCMC) methods, association studies on imputed data, and in silico investigations - genetic regions, variants, and candidate genes were identified, offering insights into the genetic etiology of EH in quilombo remnant populations.

De Novo Genome Assemblies From Two Indigenous Americans from Arizona Identify New Polymorphisms in Non-Reference Sequences

There is a collective push to diversify human genetic studies by including underrepresented populations. However, analyzing DNA sequence reads involves the initial step of aligning the reads to the GRCh38/hg38 reference genome which is inadequate for non-European ancestries. In this study, using long-read sequencing technology, we constructed de novo genome assemblies from two indigenous Americans from Arizona (IAZ). Each assembly included ∼17 Mb of DNA sequence not present [nonreference sequence (NRS)] in hg38, which consists mostly of repeat elements. Forty NRSs totaling 240 kb were uniquely anchored to the hg38 primary assembly generating a modified hg38-NRS reference genome. DNA sequence alignment and variant calling were then conducted with whole-genome sequencing (WGS) sequencing data from 387 IAZ using both the hg38 and modified hg38-NRS reference maps. Variant calling with the hg38-NRS map identified ∼50,000 single-nucleotide variants present in at least 5% of the WGS samples which were not detected with the hg38 reference map. We also directly assessed the NRSs positioned within genes. Seventeen NRSs anchored to regions including an identical 187 bp NRS found in both de novo assemblies. The NRS is located in HCN2 79 bp downstream of Exon 3 and contains several putative transcriptional regulatory elements. Genotyping of the HCN2-NRS revealed that the insertion is enriched in IAZ (minor allele frequency = 0.45) compared to other reference populations tested. This study shows that inclusion of population-specific NRSs can dramatically change the variant profile in an underrepresented ethnic groups and thereby lead to the discovery of previously missed common variations.

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.

Characterizing features affecting local ancestry inference performance in admixed populations

In recent years, significant efforts have been made to improve methods for genomic studies of admixed populations using Local Ancestry Inference (LAI). Accurate LAI is crucial to ensure downstream analyses reflect the genetic ancestry of research participants accurately. Here, we test analytic strategies for LAI to provide guidelines for optimal accuracy, focusing on admixed populations reflective of Latin America's primary continental ancestries - African (AFR), Amerindigenous (AMR), and European (EUR). Simulating LD-informed admixed haplotypes under a variety of 2 and 3-way admixture models, we implemented a standard LAI pipeline, testing three reference panel compositions to quantify their overall and ancestry-specific accuracy. We examined LAI miscall frequencies and true positive rates (TPR) across simulation models and continental ancestries. AMR tracts have notably reduced LAI accuracy as compared to EUR and AFR tracts in all comparisons, with TPR means for AMR ranging from 88-94%, EUR from 96-99% and AFR 98-99%. When LAI miscalls occurred, they most frequently erroneously called European ancestry in true Amerindigenous sites. Using a reference panel well-matched to the target population, even with a lower sample size, LAI produced true-positive estimates that were not statistically different from a high sample size but mismatched reference, while being more computationally efficient. While directly responsive to admixed Latin American cohort compositions, these trends are broadly useful for informing best practices for LAI across other admixed populations. Our findings reinforce the need for inclusion of more underrepresented populations in sequencing efforts to improve reference panels.

mtDNA "nomenclutter" and its consequences on the interpretation of genetic data

Population-based studies of human mitochondrial genetic diversity often require the classification of mitochondrial DNA (mtDNA) haplotypes into more than 5400 described haplogroups, and further grouping those into hierarchically higher haplogroups. Such secondary haplogroup groupings (e.g., "macro-haplogroups") vary across studies, as they depend on the sample quality, technical factors of haplogroup calling, the aims of the study, and the researchers' understanding of the mtDNA haplogroup nomenclature. Retention of historical nomenclature coupled with a growing number of newly described mtDNA lineages results in increasingly complex and inconsistent nomenclature that does not reflect phylogeny well. This "clutter" leaves room for grouping errors and inconsistencies across scientific publications, especially when the haplogroup names are used as a proxy for secondary groupings, and represents a source for scientific misinterpretation. Here we explore the effects of phylogenetically insensitive secondary mtDNA haplogroup groupings, and the lack of standardized secondary haplogroup groupings on downstream analyses and interpretation of genetic data. We demonstrate that frequency-based analyses produce inconsistent results when different secondary mtDNA groupings are applied, and thus allow for vastly different interpretations of the same genetic data. The lack of guidelines and recommendations on how to choose appropriate secondary haplogroup groupings presents an issue for the interpretation of results, as well as their comparison and reproducibility across studies. To reduce biases originating from arbitrarily defined secondary nomenclature-based groupings, we suggest that future updates of mtDNA phylogenies aimed for the use in mtDNA haplogroup nomenclature should also provide well-defined and standardized sets of phylogenetically meaningful algorithm-based secondary haplogroup groupings such as "macro-haplogroups", "meso-haplogroups", and "micro-haplogroups". Ideally, each of the secondary haplogroup grouping levels should be informative about different human population history events. Those phylogenetically informative levels of haplogroup groupings can be easily defined using TreeCluster, and then implemented into haplogroup callers such as HaploGrep3. This would foster reproducibility across studies, provide a grouping standard for population-based studies, and reduce errors associated with haplogroup nomenclatures in future studies.

Genetics and Genomics of Pulmonary Fibrosis: Charting the Molecular Landscape and Shaping Precision Medicine

Recent genetic and genomic advancements have elucidated the complex etiology of idiopathic pulmonary fibrosis (IPF) and other progressive fibrotic interstitial lung diseases (ILDs), emphasizing the contribution of heritable factors. This state-of-the-art review synthesizes evidence on significant genetic contributors to pulmonary fibrosis (PF), including rare genetic variants and common SNPs. The MUC5B promoter variant is unusual, a common SNP that markedly elevates the risk of early and established PF. We address the utility of genetic variation in enhancing understanding of disease pathogenesis and clinical phenotypes, improving disease definitions, and informing prognosis and treatment response. Critical research gaps are highlighted, particularly the underrepresentation of non-European ancestries in PF genetic studies and the exploration of PF phenotypes beyond usual interstitial pneumonia/IPF. We discuss the role of telomere length, often critically short in PF, and its link to progression and mortality, underscoring the genetic complexity involving telomere biology genes (TERT, TERC) and others like SFTPC and MUC5B. In addition, we address the potential of gene-by-environment interactions to modulate disease manifestation, advocating for precision medicine in PF. Insights from gene expression profiling studies and multiomic analyses highlight the promise for understanding disease pathogenesis and offer new approaches to clinical care, therapeutic drug development, and biomarker discovery. Finally, we discuss the ethical, legal, and social implications of genomic research and therapies in PF, stressing the need for sound practices and informed clinical genetic discussions. Looking forward, we advocate for comprehensive genetic testing panels and polygenic risk scores to improve the management of PF and related ILDs across diverse populations.

Selection scan in Native Americans of Mexico identifies FADS2 rs174616: Evidence of gene-diet interactions affecting lipid levels and Delta-6-desaturase activity

Searching for positive selection signals across genomes has identified functional genetic variants responding to environmental change. In Native Americans of Mexico, we used the fixation index (Fst) and population branch statistic (PBS) to identify SNPs suggesting positive selection. The 103 most differentiated SNPs were tested for associations with metabolic traits, the most significant association was FADS2/rs174616 with body mass index (BMI). This variant lies within a linkage disequilibrium (LD) block independent of previously reported FADS selection signals and has not been clearly associated with metabolic phenotypes. We tested this variant in two independent cohorts with cardiometabolic data. In the Genetics of Atherosclerotic Disease (GEA) cohort, the derived allele (T) was associated with increased BMI, lower LDL-C levels and a decreased risk of subclinical atherosclerosis in women. Significant gene-diet interactions affected lipid, apolipoprotein and adiponectin levels with differences according to sex, involving mainly total and complex dietary carbohydrate%. In the Genotype-related Effects of PUFA trial, the derived allele was associated with lower Δ-6 desaturase activity and erythrocyte membrane dihomo-gamma-linolenic acid (DGLA) levels, and with increased Δ-5 desaturase activity and eicosapentaenoic acid levels. This variant interacted with dietary carbohydrate% affecting Δ-6 desaturase activity. Notably, the relationship of DGLA and other erythrocyte membrane LC-PUFA indices with HOMA-IR differed according to rs174616 genotype, which has implications regarding how these indices should be interpreted. In conclusion, this observational study identified rs174616 as a signal suggesting selection in an independent linkage disequilibrium block, was associated with cardiometabolic and erythrocyte measurements of LC-PUFA in two independent Mexican cohorts and showed significant gene-diet interactions.

Ancestral genetic components are consistently associated with the complex trait landscape in European biobanks

The genetic structure in Europe was mostly shaped by admixture between the Western Hunter-Gatherers, Early European Farmers and Steppe Bronze Age ancestral components. Such structure is regarded as a confounder in GWAS and follow-up studies, and gold-standard methods exist to correct for it. However, it is still poorly understood to which extent these ancestral components contribute to complex trait variation in present-day Europe. In this work we harness the UK Biobank to address this question. By extensive demographic simulations, exploiting data on siblings and incorporating previous results we obtained from the Estonian Biobank, we carefully evaluate the significance and scope of our findings. Heart rate, platelet count, bone mineral density and many other traits show stratification similar to height and pigmentation traits, likely targets of selection and divergence across ancestral groups. We show that the reported ancestry-trait associations are not driven by environmental confounders by confirming our results when using between-sibling differences in ancestry. The consistency of our results across biobanks further supports this and indicates that these genetic predispositions that derive from post-Neolithic admixture events act as a source of variability and as potential confounders in Europe as a whole.

Benefit-Sharing by Design: A Call to Action for Human Genomics Research

The ethical standards for the responsible conduct of human research have come a long way; however, concerns surrounding equity remain in human genetics and genomics research. Addressing these concerns will help society realize the full potential of human genomics research. One outstanding concern is the fair and equitable sharing of benefits from research on human participants. Several international bodies have recognized that benefit-sharing can be an effective tool for ethical research conduct, but international laws, including the Convention on Biological Diversity and its Nagoya Protocol on Access and Benefit-Sharing, explicitly exclude human genetic and genomic resources. These agreements face significant challenges that must be considered and anticipated if similar principles are applied in human genomics research. We propose that benefit-sharing from human genomics research can be a bottom-up effort and embedded into the existing research process. We propose the development of a "benefit-sharing by design" framework to address concerns of fairness and equity in the use of human genomic resources and samples and to learn from the aspirations and decade of implementation of the Nagoya Protocol.

Unfulfilled Needs in the Detection, Diagnosis, Monitoring, Treatment, and Understanding of Glaucoma in Blacks Globally

Glaucoma is an ophthalmic disorder that affects a significant number of Blacks globally. A leading cause of this condition is an age-related enlargement of the lens and increased intraocular pressure. Although Blacks are affected by glaucoma at a higher rate than their Caucasian counterparts, there remains a lack of emphasis placed on the detection, diagnosis, monitoring, and treatment of glaucoma in this population. Education regarding glaucoma in the African and African American populations is essential to reducing rates of glaucoma-related visual impairment and improving treatment success. In this article, we highlight specific issues and limitations to the management of glaucoma, which affects Blacks at a higher rate. In addition, we also review the backgrounds of Blacks globally and examine historical events that have contributed to financial inequality and wealth/health disparities affecting glaucoma management. Lastly, we suggest reparations and solutions that health care professionals can use to improve glaucoma screening and management.

Sources of gene expression variation in a globally diverse human cohort

Genetic variation that influences gene expression and splicing is a key source of phenotypic diversity1-5. Although invaluable, studies investigating these links in humans have been strongly biased towards participants of European ancestries, which constrains generalizability and hinders evolutionary research. Here to address these limitations, we developed MAGE, an open-access RNA sequencing dataset of lymphoblastoid cell lines from 731 individuals from the 1000 Genomes Project6, spread across 5 continental groups and 26 populations. Most variation in gene expression (92%) and splicing (95%) was distributed within versus between populations, which mirrored the variation in DNA sequence. We mapped associations between genetic variants and expression and splicing of nearby genes (cis-expression quantitative trait loci (eQTLs) and cis-splicing QTLs (sQTLs), respectively). We identified more than 15,000 putatively causal eQTLs and more than 16,000 putatively causal sQTLs that are enriched for relevant epigenomic signatures. These include 1,310 eQTLs and 1,657 sQTLs that are largely private to underrepresented populations. Our data further indicate that the magnitude and direction of causal eQTL effects are highly consistent across populations. Moreover, the apparent 'population-specific' effects observed in previous studies were largely driven by low resolution or additional independent eQTLs of the same genes that were not detected. Together, our study expands our understanding of human gene expression diversity and provides an inclusive resource for studying the evolution and function of human genomes.

Beyond the Human Genome Project: The Age of Complete Human Genome Sequences and Pangenome References

The Human Genome Project was an enormous accomplishment, providing a foundation for countless explorations into the genetics and genomics of the human species. Yet for many years, the human genome reference sequence remained incomplete and lacked representation of human genetic diversity. Recently, two major advances have emerged to address these shortcomings: complete gap-free human genome sequences, such as the one developed by the Telomere-to-Telomere Consortium, and high-quality pangenomes, such as the one developed by the Human Pangenome Reference Consortium. Facilitated by advances in long-read DNA sequencing and genome assembly algorithms, complete human genome sequences resolve regions that have been historically difficult to sequence, including centromeres, telomeres, and segmental duplications. In parallel, pangenomes capture the extensive genetic diversity across populations worldwide. Together, these advances usher in a new era of genomics research, enhancing the accuracy of genomic analysis, paving the path for precision medicine, and contributing to deeper insights into human biology.

Exploring the relationship between admixture and genetic susceptibility to attention deficit hyperactivity disorder in two Latin American cohorts

Contemporary research on the genomics of Attention Deficit Hyperactivity Disorder (ADHD) often underrepresents admixed populations of diverse genomic ancestries, such as Latin Americans. This study explores the relationship between admixture and genetic associations for ADHD in Colombian and Mexican cohorts. Some 546 participants in two groups, ADHD and Control, were genotyped with Infinium PsychArray®. Global ancestry levels were estimated using overall admixture proportions and principal component analysis, while local ancestry was determined using a method to estimate ancestral components along the genome. Genome-wide association analysis (GWAS) was conducted to identify significant associations. Differences between Colombia and Mexico were evaluated using appropriate statistical tests. 354 Single-nucleotide polymorphisms (SNPs) and Single-nucleotide variants (SNVs) related to some genes and intergenic regions exhibited suggestive significance (p-value < 5*10e-5) in the GWAS. None of the variants revealed genome-wide significance (p-value < 5*10e-8). The study identified a significant relationship between risk SNPs and the European component of admixture, notably observed in the LOC105379109 gene. Despite differences in risk association loci, such as FOXP2, our findings suggest a possible homogeneity in genetic variation's impact on ADHD between Colombian and Mexican populations. Current reference datasets for ADHD predominantly consist of samples with high European ancestry, underscoring the need for further research to enhance the representation of reference populations and improve the identification of ADHD risk traits in Latin Americans.

AsmMix: an efficient haplotype-resolved hybrid de novo genome assembling pipeline

Accurate haplotyping facilitates distinguishing allele-specific expression, identifying cis-regulatory elements, and characterizing genomic variations, which enables more precise investigations into the relationship between genotype and phenotype. Recent advances in third-generation single-molecule long read and synthetic co-barcoded read sequencing techniques have harnessed long-range information to simplify the assembly graph and improve assembly genomic sequence. However, it remains methodologically challenging to reconstruct the complete haplotypes due to high sequencing error rates of long reads and limited capturing efficiency of co-barcoded reads. We here present a pipeline, AsmMix, for generating both contiguous and accurate diploid genomes. It first assembles co-barcoded reads to generate accurate haplotype-resolved assemblies that may contain many gaps, while the long-read assembly is contiguous but susceptible to errors. Then two assembly sets are integrated into haplotype-resolved assemblies with reduced misassembles. Through extensive evaluation on multiple synthetic datasets, AsmMix consistently demonstrates high precision and recall rates for haplotyping across diverse sequencing platforms, coverage depths, read lengths, and read accuracies, significantly outperforming other existing tools in the field. Furthermore, we validate the effectiveness of our pipeline using a human whole genome dataset (HG002), and produce highly contiguous, accurate, and haplotype-resolved assemblies. These assemblies are evaluated using the GIAB benchmarks, confirming the accuracy of variant calling. Our results demonstrate that AsmMix offers a straightforward yet highly efficient approach that effectively leverages both long reads and co-barcoded reads for haplotype-resolved assembly.

Ancestry-associated co-alteration landscape of KRAS and EGFR-altered non-squamous NSCLC

Racial/ethnic disparities mar NSCLC care and treatment outcomes. While socioeconomic factors and access to healthcare are important drivers of NSCLC disparities, a deeper understanding of genetic ancestry-associated genomic landscapes can better inform the biology and the treatment actionability for these tumors. We present a comprehensive ancestry-based prevalence and co-alteration landscape of genomic alterations and immunotherapy-associated biomarkers in patients with KRAS and EGFR-altered non-squamous (non-Sq) NSCLC. KRAS was the most frequently altered oncogene in European (EUR) and African (AFR), while EGFR alterations predominated in East Asian (EAS), South Asian (SAS), and Admixed American (AMR) groups, consistent with prior studies. As expected, STK11 and KEAP1 alterations co-occurred with KRAS alterations while showing mutual exclusivity with EGFR alterations. EAS and AMR KRAS-altered non-Sq NSCLC showed lower rates of co-occurring STK11 and KEAP1 alterations relative to other ancestry groups. Ancestry-specific co-alterations included the co-occurrence of KRAS and GNAS alterations in AMR, KRAS, and ARID1A alterations in SAS, and the mutual exclusivity of KRAS and NF1 alterations in the EUR and AFR ancestries. Contrastingly, EGFR-altered tumors exhibited a more conserved co-alteration landscape across ancestries. AFR exhibited the highest tumor mutational burden, with potential therapeutic implications for these tumors.

Methods for multiancestry genome-wide association study meta-analysis

Genome-wide association studies (GWAS) have significantly enhanced our understanding of the genetic basis of complex diseases. Despite the technological advancements, gaps in our understanding remain, partly due to small effect sizes and inadequate coverage of genetic variation. Multiancestry GWAS meta-analysis (MAGMA) addresses these challenges by integrating genetic data from diverse populations, thereby increasing power to detect loci and improving fine-mapping resolution to identify causal variants across different ancestry groups. This review provides an overview of the protocols, statistical methods, and software of MAGMA, as well as highlighting some challenges associated with this approach.

Epigenetic associations of GPNMB rs199347 variant with alcohol consumption in Parkinson's disease

Introduction

Alcohol consumption can induce a neuroinflammatory response and contribute to the progression of neurodegeneration. However, its association with Parkinson's disease (PD), the second most common neurodegenerative disorder, remains undetermined. Recent studies suggest that the glycoprotein non-metastatic melanoma protein B (GPNMB) is a potential biomarker for PD. We evaluated the association of rs199347, a variant of the GPNMB gene, with alcohol consumption and methylation upstream of GPNMB.

Methods

We retrieved genetic and DNA methylation data obtained from participants enrolled in the Taiwan Biobank (TWB) between 2008 and 2016. After excluding individuals with incomplete or missing information about potential PD risk factors, we included 1,357 participants in our final analyses. We used multiple linear regression to assess the association of GPNMB rs199347 and chronic alcohol consumption (and other potential risk factors) with GPNMB cg17274742 methylation.

Results

There was no difference between the distribution of GPNMB rs199347 genotypes between chronic alcohol consumers and the other study participants. A significant interaction was observed between the GPNMB rs199347 variant and alcohol consumption (p = 0.0102) concerning cg17274742 methylation. Compared to non-chronic alcohol consumers with the AA genotype, alcohol drinkers with the rs199347 GG genotype had significantly lower levels (hypomethylation) of cg17274742 (p = 0.0187).

Conclusion

Alcohol consumption among individuals with the rs199347 GG genotype was associated with lower levels of cg17274742 methylation, which could increase expression of the GPNMB gene, an important neuroinflammatory-related risk gene for PD.

Distinct positions of genetic and oral histories: Perspectives from India

Over the past decade, genomic data have contributed to several insights on global human population histories. These studies have been met both with interest and critically, particularly by populations with oral histories that are records of their past and often reference their origins. While several studies have reported concordance between oral and genetic histories, there is potential for tension that may stem from genetic histories being prioritized or used to confirm community-based knowledge and ethnography, especially if they differ. To investigate the interplay between oral and genetic histories, we focused on the southwestern region of India and analyzed whole-genome sequence data from 156 individuals identifying as Bunt, Kodava, Nair, and Kapla. We supplemented limited anthropological records on these populations with oral history accounts from community members and historical literature, focusing on references to non-local origins such as the ancient Scythians in the case of Bunt, Kodava, and Nair, members of Alexander the Great's army for the Kodava, and an African-related source for Kapla. We found these populations to be genetically most similar to other Indian populations, with the Kapla more similar to South Indian tribal populations that maximize a genetic ancestry related to Ancient Ancestral South Indians. We did not find evidence of additional genetic sources in the study populations than those known to have contributed to many other present-day South Asian populations. Our results demonstrate that oral and genetic histories may not always provide consistent accounts of population origins and motivate further community-engaged, multi-disciplinary investigations of non-local origin stories in these communities.

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.

Addressing Disparities in Pediatric Congenital Heart Disease: A Call for Equitable Health Care

While significant progress has been made in reducing disparities within the US health care system, notable gaps remain. This article explores existing disparities within pediatric congenital heart disease care. Congenital heart disease, the most common birth defect and a leading cause of infant death, has garnered substantial attention, revealing certain disparities within the US health care system. Factors such as race, ethnicity, insurance coverage, socioeconomic status, and geographic location are all commonalities that significantly affect health disparities in pediatric congenital heart disease. This comprehensive review sheds light on disparities from diverse perspectives in pediatric care, demonstrates the inequities and inequalities leading to these disparities, presents effective solutions, and issues a call to action for providers, institutions, and the health care system. Recognizing and addressing these disparities is imperative for ensuring equitable care and enhancing the long-term well-being of children affected by congenital heart disease. Implementing robust, evidence-based frameworks that promote responsible and safe interventions is fundamental to enduring change.

Effect of apolipoprotein E ε4 and its modification by sociodemographic characteristics on cognitive measures in South Asians from LASI-DAD

BACKGROUND

We investigated the effects of apolipoprotein E (APOE) ε4 and its interactions with sociodemographic characteristics on cognitive measures in South Asians from the Diagnostic Assessment of Dementia for the Longitudinal Aging Study of India (LASI-DAD).

METHODS

Linear regression was used to assess the association between APOE ε4 and global- and domain-specific cognitive function in 2563 participants (mean age 69.6 ± 7.3 years; 53% female). Effect modification by age, sex, and education were explored using interaction terms and subgroup analyses.

RESULTS

APOE ε4 was inversely associated with most cognitive measures (p < 0.05). This association was stronger with advancing age for the Hindi Mental State Examination (HMSE) score (βε4×age = -0.44, p = 0.03), orientation (βε4×age = -0.07, p = 0.01), and language/fluency (βε4×age = -0.07, p = 0.01), as well as in females for memory (βε4×male = 0.17, p = 0.02) and language/fluency (βε4×male = 0.12, p = 0.03).

DISCUSSION

APOE ε4 is associated with lower cognitive function in South Asians from India, with a more pronounced impact observed in females and older individuals.

HIGHLIGHTS

APOE ε4 carriers had lower global and domain-specific cognitive performance. Females and older individuals may be more susceptible to ε4 effects. For most cognitive measures, there was no interaction between ε4 and education.

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.

A systematic literature review of the 'commercialisation effect' on public attitudes towards biobank and genomic data repositories

Initiatives that collect and share genomic data to advance health research are widespread and accelerating. Commercial interests in these efforts, while vital, may erode public trust and willingness to provide personal genomic data, upon which these initiatives depend. Understanding public attitudes towards providing genomic data for health research in the context of commercial involvement is critical. A PRISMA-guided search of six online academic databases identified 113 quantitative and qualitative studies using primary data pertaining to public attitudes towards commercial actors in the management, collection, access, and use of biobank and genomic data. The presence of commercial interests yields interrelated public concerns around consent, privacy and data security, trust in science and scientists, benefit sharing, and the ownership and control of health data. Carefully considered regulatory and data governance and access policies are therefore required to maintain public trust and support for genomic health initiatives.

Increasing Diversity, Equity, Inclusion, and Accessibility in Rare Disease Clinical Trials

Diversity, equity, inclusion, and accessibility (DEIA) are foundational principles for clinical trials and medical research. In rare diseases clinical research, where numbers of participants are already challenged by rarity itself, maximizing inclusion is of particular importance to clinical trial success, as well as ensuring the generalizability and relevance of the trial results to the people affected by these diseases. In this article, we review the medical and gray literature and cite case examples to provide insights into how DEIA can be proactively integrated into rare diseases clinical research. Here, we particularly focus on genetic diversity. While the rare diseases DEIA literature is nascent, it is accelerating as many patient advocacy groups, professional societies, training and educational organizations, researcher groups, and funders are setting intentional strategies to attain DEIA goals moving forward, and to establish metrics to ensure continued improvement. Successful examples in underserved and underrepresented populations are available that can serve as case studies upon which rare diseases clinical research programs can be built. Rare diseases have historically been innovation drivers in basic, translational, and clinical research, and ultimately, all populations benefit from data diversity in rare diseases populations that deliver novel insights and approaches to how clinical research can be performed.

Clinical genetics in breast cancer

As genetic testing becomes increasingly more accessible and more applicable with a broader range of clinical implications, it may also become more challenging for breast cancer providers to remain up-to-date. This review outlines some of the current clinical guidelines and recent literature surrounding germline genetic testing, as well as genomic testing, in the screening, prevention, diagnosis, and treatment of breast cancer, while identifying potential areas of further research.

Systematic Review and Phenome-Wide Scans of Genetic Associations with Vascular Cognitive Impairment

Vascular cognitive impairment (VCI) is a heterogenous form of cognitive impairment that results from cerebrovascular disease. It is a result of both genetic and non-genetic factors. Although much research has been conducted on the genetic contributors to other forms of cognitive impairment (e.g. Alzheimer's disease), knowledge is lacking on the genetic factors associated with VCI. A better understanding of the genetics of VCI will be critical for prevention and treatment. To begin to fill this gap, the genetic contributors are reviewed with VCI from the literature. Phenome-wide scans of the identified genes are conducted and genetic variants identified in the review in large-scale resources displaying genetic variant-trait association information. Gene set are also carried out enrichment analysis using the genes identified from the review. Thirty one articles are identified meeting the search criteria and filters, from which 107 unique protein-coding genes are noted related to VCI. The phenome-wide scans and gene set enrichment analysis identify pathways associated with a diverse set of biological systems. This results indicate that genes with evidence of involvement in VCI are involved in a diverse set of biological functions. This information can facilitate downstream research to better dissect possible shared biological mechanisms for future therapies.

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.

TDC-2: Multimodal Foundation for Therapeutic Science

Therapeutics Data Commons (tdcommons.ai) is an open science initiative with unified datasets, AI models, and benchmarks to support research across therapeutic modalities and drug discovery and development stages. The Commons 2.0 (TDC-2) is a comprehensive overhaul of Therapeutic Data Commons to catalyze research in multimodal models for drug discovery by unifying single-cell biology of diseases, biochemistry of molecules, and effects of drugs through multimodal datasets, AI-powered API endpoints, new multimodal tasks and model frameworks, and comprehensive benchmarks. TDC-2 introduces over 1,000 multimodal datasets spanning approximately 85 million cells, pre-calculated embeddings from 5 state-of-the-art single-cell models, and a biomedical knowledge graph. TDC-2 drastically expands the coverage of ML tasks across therapeutic pipelines and 10+ new modalities, spanning but not limited to single-cell gene expression data, clinical trial data, peptide sequence data, peptidomimetics protein-peptide interaction data regarding newly discovered ligands derived from AS-MS spectroscopy, novel 3D structural data for proteins, and cell-type-specific protein-protein interaction networks at single-cell resolution. TDC-2 introduces multimodal data access under an API-first design using the model-view-controller paradigm. TDC-2 introduces 7 novel ML tasks with fine-grained biological contexts: contextualized drug-target identification, single-cell chemical/genetic perturbation response prediction, protein-peptide binding affinity prediction task, and clinical trial outcome prediction task, which introduce antigen-processing-pathway-specific, cell-type-specific, peptide-specific, and patient-specific biological contexts. TDC-2 also releases benchmarks evaluating 15+ state-of-the-art models across 5+ new learning tasks evaluating models on diverse biological contexts and sampling approaches. Among these, TDC-2 provides the first benchmark for context-specific learning. TDC-2, to our knowledge, is also the first to introduce a protein-peptide binding interaction benchmark.

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.

To weight or not to weight? The effect of selection bias in 3 large electronic health record-linked biobanks and recommendations for practice

OBJECTIVES

To develop recommendations regarding the use of weights to reduce selection bias for commonly performed analyses using electronic health record (EHR)-linked biobank data.

MATERIALS AND METHODS

We mapped diagnosis (ICD code) data to standardized phecodes from 3 EHR-linked biobanks with varying recruitment strategies: All of Us (AOU; n = 244 071), Michigan Genomics Initiative (MGI; n = 81 243), and UK Biobank (UKB; n = 401 167). Using 2019 National Health Interview Survey data, we constructed selection weights for AOU and MGI to represent the US adult population more. We used weights previously developed for UKB to represent the UKB-eligible population. We conducted 4 common analyses comparing unweighted and weighted results.

RESULTS

For AOU and MGI, estimated phecode prevalences decreased after weighting (weighted-unweighted median phecode prevalence ratio [MPR]: 0.82 and 0.61), while UKB estimates increased (MPR: 1.06). Weighting minimally impacted latent phenome dimensionality estimation. Comparing weighted versus unweighted phenome-wide association study for colorectal cancer, the strongest associations remained unaltered, with considerable overlap in significant hits. Weighting affected the estimated log-odds ratio for sex and colorectal cancer to align more closely with national registry-based estimates.

DISCUSSION

Weighting had a limited impact on dimensionality estimation and large-scale hypothesis testing but impacted prevalence and association estimation. When interested in estimating effect size, specific signals from untargeted association analyses should be followed up by weighted analysis.

CONCLUSION

EHR-linked biobanks should report recruitment and selection mechanisms and provide selection weights with defined target populations. Researchers should consider their intended estimands, specify source and target populations, and weight EHR-linked biobank analyses accordingly.

The somatic mutation profile of estrogen receptor-positive HER2-negative metastatic breast cancer in Brazilian patients

Background

Breast cancer is the leading cause of cancer death among women worldwide. Studies about the genomic landscape of metastatic breast cancer (MBC) have predominantly originated from developed nations. There are still limited data on the molecular epidemiology of MBC in low- and middle-income countries. This study aims to evaluate the prevalence of mutations in the PI3K-AKT pathway and other actionable drivers in estrogen receptor (ER)+/HER2- MBC among Brazilian patients treated at a large institution representative of the nation's demographic diversity.

Methods

We conducted a retrospective observational study using laboratory data (OC Precision Medicine). Our study included tumor samples from patients with ER+/HER2- MBC who underwent routine tumor testing from 2020 to 2023 and originated from several Brazilian centers within the Oncoclinicas network. Two distinct next-generation sequencing (NGS) assays were used: GS Focus (23 genes, covering PIK3CA, AKT1, ESR1, ERBB2, BRCA1, BRCA2, PALB2, TP53, but not PTEN) or GS 180 (180 genes, including PTEN, tumor mutation burden [TMB] and microsatellite instability [MSI]).

Results

Evaluation of tumor samples from 328 patients was undertaken, mostly (75.6%) with GS Focus. Of these, 69% were primary tumors, while 31% were metastatic lesions. The prevalence of mutations in the PI3K-AKT pathway was 39.3% (95% confidence interval, 33% to 43%), distributed as 37.5% in PIK3CA and 1.8% in AKT1. Stratification by age revealed a higher incidence of mutations in this pathway among patients over 50 (44.5% vs 29.1%, p=0.01). Among the PIK3CA mutations, 78% were canonical (included in the alpelisib companion diagnostic non-NGS test), while the remaining 22% were characterized as non-canonical mutations (identifiable only by NGS test). ESR1 mutations were detected in 6.1%, exhibiting a higher frequency in metastatic samples (15.1% vs 1.3%, p=0.003). Additionally, mutations in BRCA1, BRCA2, or PALB2 were identified in 3.9% of cases, while mutations in ERBB2 were found in 2.1%. No PTEN mutations were detected, nor were TMB high or MSI cases.

Conclusion

We describe the genomic landscape of Brazilian patients with ER+/HER2- MBC, in which the somatic mutation profile is comparable to what is described in the literature globally. These data are important for developing precision medicine strategies in this scenario, as well as for health systems management and research initiatives.

Developing an optimal stratification model for colorectal cancer screening and reducing racial disparities in multi-center population-based studies

BACKGROUND

Early detection of colorectal neoplasms can reduce the colorectal cancer (CRC) burden by timely intervention for high-risk individuals. However, effective risk prediction models are lacking for personalized CRC early screening in East Asian (EAS) population. We aimed to develop, validate, and optimize a comprehensive risk prediction model across all stages of the dynamic adenoma-carcinoma sequence in EAS population.

METHODS

To develop precision risk-stratification and intervention strategies, we developed three trans-ancestry PRSs targeting colorectal neoplasms: (1) using 148 previously identified CRC risk loci (PRS148); (2) SNPs selection from large-scale meta-analysis data by clumping and thresholding (PRS183); (3) PRS-CSx, a Bayesian approach for genome-wide risk prediction (PRSGenomewide). Then, the performance of each PRS was assessed and validated in two independent cross-sectional screening sets, including 4600 patients with advanced colorectal neoplasm, 4495 patients with non-advanced adenoma, and 21,199 normal individuals from the ZJCRC (Zhejiang colorectal cancer set; EAS) and PLCO (the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial; European, EUR) studies. The optimal PRS was further incorporated with lifestyle factors to stratify individual risk and ultimately tested in the PLCO and UK Biobank prospective cohorts, totaling 350,013 participants.

RESULTS

Three trans-ancestry PRSs achieved moderately improved predictive performance in EAS compared to EUR populations. Remarkably, the PRSs effectively facilitated a thorough risk assessment across all stages of the dynamic adenoma-carcinoma sequence. Among these models, PRS183 demonstrated the optimal discriminatory ability in both EAS and EUR validation datasets, particularly for individuals at risk of colorectal neoplasms. Using two large-scale and independent prospective cohorts, we further confirmed a significant dose-response effect of PRS183 on incident colorectal neoplasms. Incorporating PRS183 with lifestyle factors into a comprehensive strategy improves risk stratification and discriminatory accuracy compared to using PRS or lifestyle factors separately. This comprehensive risk-stratified model shows potential in addressing missed diagnoses in screening tests (best NPV = 0.93), while moderately reducing unnecessary screening (best PPV = 0.32).

CONCLUSIONS

Our comprehensive risk-stratified model in population-based CRC screening trials represents a promising advancement in personalized risk assessment, facilitating tailored CRC screening in the EAS population. This approach enhances the transferability of PRSs across ancestries and thereby helps address health disparity.

Effects of Implicit Racial Bias and Standardized Patient Race on Genetic Counseling Students' Patient-Centered Communication

Clinician racial bias has been associated with less patient-centered communication, but little is known about how it affects trainees' communication. We investigated genetic counseling students' communication during sessions with Black or White standardized patients (SPs) and the extent to which communication was associated with SP race or student scores on the Race Implicit Association Test (IAT). Sixty students conducted a baseline SP session and up to two follow-up sessions. Students were randomly assigned to a different White or Black SP and one of three clinical scenarios for each session. Fifty-six students completed the IAT. Session recordings were coded using the Roter Interaction Analysis System. Linear regression models assessed the effects of IAT score and SP race on a variety of patient-centered communication indicators. Random intercept models assessed the within-student effects of SP race on communication outcomes during the baseline session and in follow-up sessions (n = 138). Students were predominantly White (71%). Forty students (71%) had IAT scores indicating some degree of pro-White implicit preference. Baseline sessions with White relative to Black SPs had higher patient-centeredness scores. Within-participant analyses indicate that students used a higher proportion of back-channels (a facilitative behavior that cues interest and encouragement) and conducted longer sessions with White relative to Black SPs. Students' stronger pro-White IAT scores were associated with using fewer other facilitative statements during sessions with White relative to Black SPs. Different patterns of communication associated with SP race and student IAT scores were found for students than those found in prior studies with experienced clinicians.

shaPRS: Leveraging shared genetic effects across traits or ancestries improves accuracy of polygenic scores

We present shaPRS, a method that leverages widespread pleiotropy between traits or shared genetic effects across ancestries, to improve the accuracy of polygenic scores. The method uses genome-wide summary statistics from two diseases or ancestries to improve the genetic effect estimate and standard error at SNPs where there is homogeneity of effect between the two datasets. When there is significant evidence of heterogeneity, the genetic effect from the disease or population closest to the target population is maintained. We show via simulation and a series of real-world examples that shaPRS substantially enhances the accuracy of polygenic risk scores (PRSs) for complex diseases and greatly improves PRS performance across ancestries. shaPRS is a PRS pre-processing method that is agnostic to the actual PRS generation method, and as a result, it can be integrated into existing PRS generation pipelines and continue to be applied as more performant PRS methods are developed over time.

Mapping the Pharmacogenetic Landscape in a Ugandan Population: Implications for Personalized Medicine in an Underrepresented Population

Africans are extremely underrepresented in global genomic research. African populations face high burdens of communicable and non-communicable diseases and experience widespread polypharmacy. As population-specific genetic studies are crucial to understanding unique genetic profiles and optimizing treatments to reduce medication-related complications in this diverse population, the present study aims to characterize the pharmacogenomics profile of a rural Ugandan population. We analyzed low-pass whole genome sequencing data from 1998 Ugandans to investigate 18 clinically actionable pharmacogenes in this population. We utilized PyPGx to identify star alleles (haplotype patterns) and compared allele frequencies across populations using the Pharmacogenomics Knowledgebase PharmGKB. Clinical interpretations of the identified alleles were conducted following established dosing guidelines. Over 99% of participants displayed actionable phenotypes across the 18 pharmacogenes, averaging 3.5 actionable genotypes per individual. Several variant alleles known to affect drug metabolism (i.e., CYP3A5*1, CYP2B6*9, CYP3A5*6, CYP2D6*17, CYP2D6*29, and TMPT*3C)-which are generally more prevalent in African individuals-were notably enriched in the Ugandan cohort, beyond reported frequencies in other African peoples. More than half of the cohort exhibited a predicted impaired drug response associated with CFTR, IFNL3, CYP2B6, and CYP2C19, and approximately 31% predicted altered CYP2D6 metabolism. Potentially impaired CYP2C9, SLCO1B1, TPMT, and DPYD metabolic phenotypes were also enriched in Ugandans compared with other African populations. Ugandans exhibit distinct allele profiles that could impact drug efficacy and safety. Our findings have important implications for pharmacogenomics in Uganda, particularly with respect to the treatment of prevalent communicable and non-communicable diseases, and they emphasize the potential of pharmacogenomics-guided therapies to optimize healthcare outcomes and precision medicine in Uganda.

The VEGF-Hypoxia Signature Is Upregulated in Basal-like Breast Tumors from Women of African Ancestry and Associated with Poor Outcomes in Breast Cancer

PURPOSE

Black women experience the highest breast cancer mortality rate compared with women of other racial/ethnic groups. To gain a deeper understanding of breast cancer heterogeneity across diverse populations, we examined a VEGF-hypoxia gene expression signature in breast tumors from women of diverse ancestry.

EXPERIMENTAL DESIGN

We developed a NanoString nCounter gene expression panel and applied it to breast tumors from Nigeria (n = 182) and the University of Chicago (Chicago, IL; n = 161). We also analyzed RNA sequencing data from Nigeria (n = 84) and The Cancer Genome Atlas (TCGA) datasets (n = 863). Patient prognosis was analyzed using multiple datasets.

RESULTS

The VEGF-hypoxia signature was highest in the basal-like subtype compared with other subtypes, with greater expression in Black women compared with White women. In TCGA dataset, necrotic breast tumors had higher scores for the VEGF-hypoxia signature compared with non-necrosis tumors (P < 0.001), with the highest proportion in the basal-like subtype. Furthermore, necrotic breast tumors have higher scores for the proliferation signature, suggesting an interaction between the VEGF-hypoxia signature, proliferation, and necrosis. T-cell gene expression signatures also correlated with the VEGF-hypoxia signature when testing all tumors in TCGA dataset. Finally, we found a significant association of the VEGF-hypoxia profile with poor outcomes when using all patients in the METABRIC (P < 0.0001) and SCAN-B datasets (P = 0.002).

CONCLUSIONS

These data provide further evidence for breast cancer heterogeneity across diverse populations and molecular subtypes. Interventions selectively targeting VEGF-hypoxia and the immune microenvironment have the potential to improve overall survival in aggressive breast cancers that disproportionately impact Black women in the African Diaspora.

Alleviating misclassified germline variants in underrepresented populations: A strategy using popmax

PURPOSE

Germline variant interpretation often depends on population-matched control cohorts. This is not feasible for population groups that are underrepresented in current population reference databases.

METHODS

We classify germline variants with population-matched controls for 2 ancestrally diverse cohorts of patients: 132 early-onset or familial colorectal carcinoma patients from Singapore and 100 early-onset colorectal carcinoma patients from the United States. The effects of using a population-mismatched control cohort are simulated by swapping the control cohorts used for each patient cohort, with or without the popmax computational strategy.

RESULTS

Population-matched classifications revealed a combined 62 pathogenic or likely pathogenic (P/LP) variants in 34 genes across both cohorts. Using a population-mismatched control cohort resulted in misclassification of non-P/LP variants as P/LP, driven by the absence of ancestry-specific rare variants in the control cohort. Popmax was more effective in alleviating misclassifications for the Singapore cohort than the US cohort.

CONCLUSION

Underrepresented population groups can suffer from higher rates of false-positive P/LP results. Popmax can partially alleviate these misclassifications, but its efficacy still depends on the degree with which the population groups are represented in the control cohort.

Improving access to exome sequencing in a medically underserved population through the Texome Project

PURPOSE

Genomic medicine can end diagnostic odysseys for patients with complex phenotypes; however, limitations in insurance coverage and other systemic barriers preclude individuals from accessing comprehensive genetics evaluation and testing.

METHODS

The Texome Project is a 4-year study that reduces barriers to genomic testing for individuals from underserved and underrepresented populations. Participants with undiagnosed, rare diseases who have financial barriers to obtaining exome sequencing (ES) clinically are enrolled in the Texome Project.

RESULTS

We highlight the Texome Project process and describe the outcomes of the first 60 ES results for study participants. Participants received a genetic evaluation, ES, and return of results at no cost. We summarize the psychosocial or medical implications of these genetic diagnoses. Thus far, ES provided molecular diagnoses for 18 out of 60 (30%) of Texome participants. Plus, in 11 out of 60 (18%) participants, a partial or probable diagnosis was identified. Overall, 5 participants had a change in medical management.

CONCLUSION

To date, the Texome Project has recruited a racially, ethnically, and socioeconomically diverse cohort. The diagnostic rate and medical impact in this cohort support the need for expanded access to genetic testing and services. The Texome Project will continue reducing barriers to genomic care throughout the future study years.

Vitamin D deficiency and VDR gene polymorphism FokI (rs2228570) are associated with diabetes mellitus in adults: COVID-inconfidentes study

BACKGROUND

Diabetes mellitus is a chronic and multifactorial condition, including environmental risk factors such as lifestyle habits and genetic conditions.

OBJECTIVE

We aimed to evaluate the association of VDR gene polymorphism (rs2228570) FokI and vitamin D levels with diabetes in adults.

METHODS

Cross-sectional population-based study in adults, conducted from October to December 2020 in two Brazilian cities. The outcome variable was diabetes, defined as glycated hemoglobin ≥ 6.5% or self-report medical diagnosis or use of oral hypoglycemic drugs. Vitamin D (25-hydroxyvitamin D) was measured by indirect electrochemiluminescence, and classified as deficiency when 25(OH)D < 20 ng/mL. All participants were genotyped for VDR FokI polymorphism by qPCR and classified as homozygous mutant (ff or GG), heterozygous (Ff or AG), or homozygous wild (FF or AA). A combined analysis between the FokI polymorphism and vitamin D levels with diabetes was also examined. A directed acyclic graph (DAG) was used to select minimal and sufficient adjustment for confounding variables by the backdoor criterion.

RESULTS

The prevalence of DM was 9.4% and vitamin D deficiency (VDD) was 19.9%. The genotype distribution of FokI polymorphism was 9.9% FF, 44.8% Ff, and 45.3% ff. It was possible to verify a positive association between vitamin D deficiency and DM (OR = 2.19; 95% CI: 1.06-4.50). Individuals with the altered allele (ff) had a 1.78 higher prevalence of DM (OR: 1.78; 95% CI; 1.10-2.87). Combined analyses, individuals with vitamin D deficiency and one or two copies of the altered FokI allele had a higher prevalence of DM (Ff + ff: OR: 1.67; 95% CI; 1.07-2.61; ff: OR: 3.60; 95% CI; 1.40-9.25).

CONCLUSION

Our data suggest that vitamin D deficiency and FokI polymorphism are associated with DM.

AI approaches for the discovery and validation of drug targets

Artificial intelligence (AI) holds immense promise for accelerating and improving all aspects of drug discovery, not least target discovery and validation. By integrating a diverse range of biological data modalities, AI enables the accurate prediction of drug target properties, ultimately illuminating biological mechanisms of disease and guiding drug discovery strategies. Despite the indisputable potential of AI in drug target discovery, there are many challenges and obstacles yet to be overcome, including dealing with data biases, model interpretability and generalisability, and the validation of predicted drug targets, to name a few. By exploring recent advancements in AI, this review showcases current applications of AI for drug target discovery and offers perspectives on the future of AI for the discovery and validation of drug targets, paving the way for the generation of novel and safer pharmaceuticals.

Characterizing substructure via mixture modeling in large-scale genetic summary statistics

Genetic summary data are broadly accessible and highly useful including for risk prediction, causal inference, fine mapping, and incorporation of external controls. However, collapsing individual-level data into groups masks intra- and inter-sample heterogeneity, leading to confounding, reduced power, and bias. Ultimately, unaccounted substructure limits summary data usability, especially for understudied or admixed populations. Here, we present Summix2, a comprehensive set of methods and software based on a computationally efficient mixture model to estimate and adjust for substructure in genetic summary data. In extensive simulations and application to public data, Summix2 characterizes finer-scale population structure, identifies ascertainment bias, and identifies potential regions of selection due to local substructure deviation. Summix2 increases the robust use of diverse publicly available summary data resulting in improved and more equitable research.

Using genome and transcriptome data from African-ancestry female participants to identify putative breast cancer susceptibility genes

African-ancestry (AA) participants are underrepresented in genetics research. Here, we conducted a transcriptome-wide association study (TWAS) in AA female participants to identify putative breast cancer susceptibility genes. We built genetic models to predict levels of gene expression, exon junction, and 3' UTR alternative polyadenylation using genomic and transcriptomic data generated in normal breast tissues from 150 AA participants and then used these models to perform association analyses using genomic data from 18,034 cases and 22,104 controls. At Bonferroni-corrected P < 0.05, we identified six genes associated with breast cancer risk, including four genes not previously reported (CTD-3080P12.3, EN1, LINC01956 and NUP210L). Most of these genes showed a stronger association with risk of estrogen-receptor (ER) negative or triple-negative than ER-positive breast cancer. We also replicated the associations with 29 genes reported in previous TWAS at P < 0.05 (one-sided), providing further support for an association of these genes with breast cancer risk. Our study sheds new light on the genetic basis of breast cancer and highlights the value of conducting research in AA populations.

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.

Deciphering the Interplay between Genetic Risk Scores and Lifestyle Factors on Individual Obesity Predisposition

Obesity's variability is significantly influenced by the interplay between genetic and environmental factors. We aimed to integrate the combined impact of genetic risk score (GRSBMI) with physical activity (PA), sugar-sweetened beverages (SSB), wine intake, and eating habits score (EHS) on obesity predisposition risk. Adults' (n = 5824) data were analyzed for common obesity-related single nucleotide polymorphisms and lifestyle habits. The weighted GRSBMI was constructed and categorized into quartiles (Qs), and the adjusted multivariate logistic regression models examined the association of GRSBMI with obesity (BMI ≥ 30) and lifestyle factors. GRSBMI was significantly associated with obesity risk. Each GRSBMI unit was associated with an increase of 3.06 BMI units (p ≤ 0.0001). PA markedly reduced obesity risk across GRSBMI Qs. Inactive participants' (≥90 min/week) mean BMI was higher in GRSBMI Q3-Q4 compared to Q1 (p = 0.003 and p < 0.001, respectively). Scoring EHS ≥ median, SSBs (≥1 cup/day), and non-wine drinking were associated with higher BMI within all GRSBMI Qs compared to EHS < median, non-SSBs, and non-wine drinkers. Mean BMI was higher in GRSBMI Q4 compared to other quartiles (p < 0.0001) in non-wine drinkers and compared to Q1 for SSB's consumers (p = 0.07). A higher GRSBMI augmented the impact of lifestyle factors on obesity. The interplay between GRSBMI and modifiable lifestyle factors provides a tailored personalized prevention and treatment for obesity management.