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The All of Us Research Program ("All of Us") is an initiative led by the National Institutes of Health whose goal is to advance research on personalized medicine and health equity through the collection of genetic, environmental, demographic, and health data from volunteer participants who reside in the USA. The program's emphasis on recruiting a diverse participant cohort makes "All of Us" an effective platform for investigating health disparities. In this work, we analyzed participant electronic health record (EHR) data to identify the diseases and disease categories in the "All of Us" cohort for which racial and ethnic prevalence disparities can be observed. In conjunction with these analyses, we developed the US Health Disparities Browser as an interactive web application that enables users to visualize differences in race- and ethnic-group-specific prevalence estimates for 1755 different diseases: https://usdisparities.biosci.gatech.edu/. The web application features a catalog of all diseases represented in the browser, which can be sorted by overall prevalence as well as the variance in prevalence across racial and ethnic groups. The analyses outlined here provide details on the nature and extent of racial and ethnic health disparities in the "All of Us" participant cohort, and the accompanying browser can serve as a resource through which researchers can explore these disparities Database URL: https://usdisparities.biosci.gatech.edu.

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Racial biases, which harm marginalized and excluded communities, may be combatted by clarifying misconceptions about race during biology lessons. We developed a human genetics laboratory activity that challenges the misconception that race is biological (biological essentialism). We assessed the relationship between this activity and student outcomes using a survey of students' attitudes about biological essentialism and color-evasive ideology and a concept inventory about phylogeny and human diversity. Students in the human genetics laboratory activity showed a significant decrease in their acceptance of biological essentialism compared with a control group, but did not show changes in color-evasive ideology. Students in both groups exhibited increased knowledge in both areas of the concept inventory, but the gains were larger in the human genetics laboratory. In the second iteration of this activity, we found that only white students' decreases in biological essentialist beliefs were significant and the activity failed to decrease color-evasive ideologies for all students. Concept inventory gains were similar and significant for both white and non-white students in this iteration. Our findings underscore the effectiveness of addressing misconceptions about the biological origins of race and encourage more research on ways to effectively change damaging student attitudes about race in undergraduate genetics education.

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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.

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

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The KRAS mutation is the most common oncogenic driver in patients with non-small cell lung cancer (NSCLC). However, a detailed understanding of how self-reported race and/or ethnicity (SIRE), genetically inferred ancestry (GIA), and their interaction affect KRAS mutation is largely unknown. Here, we investigated the associations between SIRE, quantitative GIA, and KRAS mutation and its allele-specific subtypes in a multi-ethnic cohort of 3,918 patients from the Boston Lung Cancer Survival cohort and the Chinese OrigiMed cohort with an independent validation cohort of 1,450 patients with NSCLC. This comprehensive analysis included detailed covariates such as age at diagnosis, sex, clinical stage, cancer histology, and smoking status. We report that SIRE is significantly associated with KRAS mutations, modified by sex, with SIRE-Asian patients showing lower rates of KRAS mutation, transversion substitution, and the allele-specific subtype KRASG12C compared to SIRE-White patients after adjusting for potential confounders. Moreover, GIA was found to correlate with KRAS mutations, where patients with a higher proportion of European ancestry had an increased risk of KRAS mutations, especially more transition substitutions and KRASG12D. Notably, among SIRE-White patients, an increase in European ancestry was linked to a higher likelihood of KRAS mutations, whereas an increase in admixed American ancestry was associated with a reduced likelihood, suggesting that quantitative GIA offers additional information beyond SIRE. The association of SIRE, GIA, and their interplay with KRAS driver mutations in NSCLC highlights the importance of incorporating both into population-based cancer research, aiming to refine clinical decision-making processes and mitigate health disparities.

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Biological trace samples consisting of very few cells pose a challenge to conventional forensic genetic DNA analysis. RNA may be an alternative to DNA when handling low template samples. Whereas each cell only contains two copies of an autosomal DNA segment, the transcriptome retains much of the genomic variation replicated in abundant RNA fragments. In this study, we describe the development of a prototype RNA-based SNP selection set for forensic human identification from low template samples (50 pg gDNA). Whole blood from a subset of the Danish population (41 individuals) and blood stains subjected to degradation at room temperature for up to two weeks were analysed by whole transcriptome shotgun sequencing. Concordance was determined by DNA genotyping with the Infinium Omni5-4 SNP chip. In the 100 protein-coding genes with the most reads, 5214 bi-allelic SNPs with gnomAD minor allele frequencies > 0.1 in the African/African American, East Asian, and (non-Finnish) European populations were identified. Of these, 24 SNPs in 21 genes passed screening in whole blood and degraded blood stains, with a resulting mean match probability of 4.5 ∙ 10-9. Additionally, ancestry informative SNPs and SNPs in genes useful for body fluid identification were identified in the transcriptome. Consequently, shotgun sequencing of RNA from low template samples may be used for a vast host of forensic genetics purposes, including simultaneous human and body fluid identification, leading to direct donor identification in the identified body fluid.

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PURPOSE: Ethnic diversity in cancer research is crucial as race/ethnicity influences cancer incidence, survival, drug response, molecular pathways, and epigenetic phenomena. In 2018, we began a project to examine racial/ethnic diversity in cancer research, with a commitment to review these disparities every 4 years. This report is our second assessment, detailing the present state of racial/ethnic diversity in cancer genomics and clinical trials. METHODS: To study racial/ethnic inclusion in cancer genomics, we extracted ethnic records from all data sets available at cBioPortal (n = 125,128 patients) and cancer-related genome-wide association studies (n = 28,011,282 patients) between 2018 and 2022. Concerning clinical trials, we selected studies related to breast cancer (n = 125,518 patients, 181 studies), lung cancer (n = 34,329 patients, 119 studies), and colorectal cancer (n = 40,808 patients, 105 studies). RESULTS: In cancer genomics (N = 28,136,410), 3% of individuals lack racial/ethnic registries; tumor samples were collected predominantly from White patients (89.14%), followed by Asian (7%), African American (0.55%), and Hispanic (0.21%) patients and other populations (0.1%). In clinical trials (N = 200,655), data on race/ethnicity are missing for 60.14% of the participants; for individuals whose race/ethnicity was recorded, most were characterized as White (28.33%), followed by Asian (7.64%), African (1.79), other ethnicities (1.37), and Hispanic (0.73). Racial/ethnic representation significantly deviates from global ethnic proportions (P ≤ .001) across all data sets, with White patients outnumbering other ethnic groups by a factor of approximately 4-6. CONCLUSION: Our second update on racial/ethnic representation in cancer research highlights the persistent overrepresentation of White populations in cancer genomics and a notable absence of racial/ethnic information across clinical trials. To ensure more equitable and effective precision oncology, future efforts should address the reasons behind the insufficient representation of ethnically diverse populations in cancer research.

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OBJECTIVE: The influence of socioeconomic factors on racial disparities among patients with sporadic meningiomas is well established, yet other potential causative factors warrant further exploration. The authors of this study aimed to determine whether there is significant variation in the genomic profile of meningiomas among patients of different races and ethnicities and its correlation with clinical outcomes. METHODS: The demographic, genomic, and clinical data of patients aged 18 years and older who had undergone surgery for sporadic meningioma between September 2008 and November 2021 were analyzed. Statistical analyses were performed to detect differences across all racial/ethnic groups, as were direct comparisons between Black and non-Black groups plus Hispanic and non-Hispanic groups. RESULTS: This study included 460 patients with intracranial meningioma. Hispanic patients were significantly younger at surgery (53.9 vs 60.2 years, p = 0.0006) and more likely to show symptoms. Black patients had a higher incidence of anterior skull base tumors (OR 3.2, 95% CI 1.7-6.3, p = 0.0008) and somatic hedgehog mutations (OR 5.3, 95% CI 1.6-16.6, p = 0.003). Hispanics were less likely to exhibit the aggressive genomic characteristic of chromosome 1p deletion (OR 0.28, 95% CI 0.07-1.2, p = 0.06) and displayed higher rates of TRAF7 somatic driver mutations (OR 2.96 95% CI 1.1-7.8, p = 0.036). Black patients had higher rates of recurrence (OR 2.6, 95% CI 1.3-5.2, p = 0.009) and shorter progression-free survival (PFS; HR 2.9, 95% CI 1.6-5.4, p = 0.002) despite extents of resection (EORs) similar to those of non-Black patients (p = 0.745). No significant differences in overall survival were observed among groups. CONCLUSIONS: Despite similar EORs, Black patients had worse clinical outcomes following meningioma resection, characterized by a higher prevalence of somatic hedgehog mutations, increased recurrence rates, and shorter PFS. Meanwhile, Hispanic patients had less aggressive meningiomas, a predisposition for TRAF7 mutations, and no difference in PFS. These findings could inform the care and treatment strategies for meningiomas, and they establish the foundation for future studies focusing on the genomic origins of these observed differences.

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This Medical News story discusses new findings from genomics studies involving diverse cohorts.

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Comprehensively mapping the genetic basis of human disease across diverse individuals is a long-standing goal for the field of human genetics1-4. The All of Us Research Program is a longitudinal cohort study aiming to enrol a diverse group of at least one million individuals across the USA to accelerate biomedical research and improve human health5,6. Here we describe the programme's genomics data release of 245,388 clinical-grade genome sequences. This resource is unique in its diversity as 77% of participants are from communities that are historically under-represented in biomedical research and 46% are individuals from under-represented racial and ethnic minorities. All of Us identified more than 1 billion genetic variants, including more than 275 million previously unreported genetic variants, more than 3.9 million of which had coding consequences. Leveraging linkage between genomic data and the longitudinal electronic health record, we evaluated 3,724 genetic variants associated with 117 diseases and found high replication rates across both participants of European ancestry and participants of African ancestry. Summary-level data are publicly available, and individual-level data can be accessed by researchers through the All of Us Researcher Workbench using a unique data passport model with a median time from initial researcher registration to data access of 29 hours. We anticipate that this diverse dataset will advance the promise of genomic medicine for all.

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The use of genetic markers, specifically Short Tandem Repeats (STRs), has been a valuable tool for identifying persons of interest. However, the ability to analyze additional markers including Single Nucleotide Polymorphisms (SNPs) and Insertion/Deletion (INDELs) polymorphisms allows laboratories to explore other investigative leads. INDELs were chosen in this study because large panels can be differentiated by size, allowing them to be genotyped by capillary electrophoresis. Moreover, these markers do not produce stutter and are smaller in size than STRs, facilitating the recovery of genetic information from degraded samples. The INDEL Ancestry Informative Markers (AIMs) in this study were selected from the 1000 Genomes Project based on a fixation index (FST) greater than 0.50, high allele frequency divergence, and genetic distance. A total of 25 INDEL-AIMs were optimized and validated according to SWGDAM guidelines in a five-dye multiplex. To validate the panel, genotyping was performed on 155 unrelated individuals from four ancestral groups (Caucasian, African, Hispanic, and East Asian). Bayesian clustering and principal component analysis (PCA) were performed revealing clear separation among three groups, with some observed overlap within the Hispanic group. Additionally, the PCA results were compared against a training set of 793 samples from the 1000 Genomes Project, demonstrating consistent results. Validation studies showed the assay to be reproducible, tolerant to common inhibitors, robust with challenging casework type samples, and sensitive down to 125 pg. In conclusion, our results demonstrated the robustness and effectiveness of a 25 loci INDEL system for ancestry inference of four ancestries commonly found in the United States.

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Differences in cancer genomes between racial groups may impact tumor biology and health disparities. However, the discovery of race-associated mutations is constrained by the limited representation and sample size of different racial groups in prior genomic studies. We evaluated the influence of race on the frequency of gene mutations using the Genomics, Evidence, Neoplasia, Information, Exchange database, a large genomic dataset aggregated from clinical sequencing. Matched cohort analyses were used to identify histology-specific race-associated mutations including increased TERT promoter mutations in Black and Asian patients with gliomas and bladder cancers, and a decreased frequency of mutations in DNA repair pathway genes and subunits of the SWI/SNF chromatin complex in Asian and Black patients across multiple cancer types. The distribution of actionable mutations in oncogenes was also race-specific, demonstrating how targeted therapies may have a disparate impact on racial groups. Down-sampling analyses indicate that larger sample sizes are likely to discover more race-associated mutations. These results provide a resource to understand differences in cancer genomes between racial groups which may inform the design of clinical studies and patient recruitment strategies in biomarker trials.

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The heritability of susceptibility to tuberculosis (TB) disease has been well recognized. Over 100 genes have been studied as candidates for TB susceptibility, and several variants were identified by genome-wide association studies (GWAS), but few replicate. We established the International Tuberculosis Host Genetics Consortium to perform a multi-ancestry meta-analysis of GWAS, including 14,153 cases and 19,536 controls of African, Asian, and European ancestry. Our analyses demonstrate a substantial degree of heritability (pooled polygenic h2 = 26.3%, 95% CI 23.7-29.0%) for susceptibility to TB that is shared across ancestries, highlighting an important host genetic influence on disease. We identified one global host genetic correlate for TB at genome-wide significance (p<5 × 10-8) in the human leukocyte antigen (HLA)-II region (rs28383206, p-value=5.2 × 10-9) but failed to replicate variants previously associated with TB susceptibility. These data demonstrate the complex shared genetic architecture of susceptibility to TB and the importance of large-scale GWAS analysis across multiple ancestries experiencing different levels of infection pressure.

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