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Recent studies have demonstrated that polygenic risk scores (PRS) trained on multi-ancestry data can improve prediction accuracy in groups historically underrepresented in genomic studies, but the availability of linked health and genetic data from large-scale diverse cohorts representative of a wide spectrum of human diversity remains limited. To address this need, the All of Us research program (AoU) generated whole-genome sequences of 245,388 individuals who collectively reflect the diversity of the USA. Leveraging this resource and another widely-used population-scale biobank, the UK Biobank (UKB) with a half million participants, we developed PRS trained on multi-ancestry and multi-biobank data with up to â¼750,000 participants for 32 common, complex traits and diseases across a range of genetic architectures. We then compared effects of ancestry, PRS methodology, and genetic architecture on PRS accuracy across a held out subset of ancestrally diverse AoU participants. Due to the more heterogeneous study design of AoU, we found lower heritability on average compared to UKB (0.075 vs 0.165), which limited the maximal achievable PRS accuracy in AoU. Overall, we found that the increased diversity of AoU significantly improved PRS performance in some participants in AoU, especially underrepresented individuals, across multiple phenotypes. Notably, maximizing sample size by combining discovery data across AoU and UKB is not the optimal approach for predicting some phenotypes in African ancestry populations; rather, using data from only AoU for these traits resulted in the greatest accuracy. This was especially true for less polygenic traits with large ancestry-enriched effects, such as neutrophil count ( R 2 : 0.055 vs. 0.035 using AoU vs. cross-biobank meta-analysis, respectively, because of e.g. DARC ). Lastly, we calculated individual-level PRS accuracies rather than grouping by continental ancestry, a critical step towards interpretability in precision medicine. Individualized PRS accuracy decays linearly as a function of ancestry divergence, but the slope was smaller using multi-ancestry GWAS compared to using European GWAS. Our results highlight the potential of biobanks with more balanced representations of human diversity to facilitate more accurate PRS for the individuals least represented in genomic studies.
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Evidence for adaptation of human skin color to regional ultraviolet radiation suggests shared and distinct genetic variants across populations. However, skin color evolution and genetics in East Asians are understudied. We quantified skin color in 48,433 East Asians using image analysis and identified associated genetic variants and potential causal genes for skin color as well as their polygenic interplay with sun exposure. This genome-wide association study (GWAS) identified 12 known and 11 previously unreported loci and SNP-based heritability was 23-24%. Potential causal genes were determined through the identification of nonsynonymous variants, colocalization with gene expression in skin tissues, and expression levels in melanocytes. Genomic loci associated with pigmentation in East Asians substantially diverged from European populations, and we detected signatures of polygenic adaptation. This large GWAS for objectively quantified skin color in an East Asian population improves understanding of the genetic architecture and polygenic adaptation of skin color and prioritizes potential causal genes.
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Estudo de Associação Genômica Ampla , Herança Multifatorial , Polimorfismo de Nucleotídeo Único , Pigmentação da Pele , Adulto , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Adaptação Fisiológica/genética , Mapeamento Cromossômico , Herança Multifatorial/genética , Locos de Características Quantitativas/genética , Pigmentação da Pele/genética , Raios Ultravioleta , População do Leste AsiáticoRESUMO
Polygenic scores (PGSs) offer the ability to predict genetic risk for complex diseases across the life course; a key benefit over short-term prediction models. To produce risk estimates relevant to clinical and public health decision-making, it is important to account for varying effects due to age and sex. Here, we develop a novel framework to estimate country-, age-, and sex-specific estimates of cumulative incidence stratified by PGS for 18 high-burden diseases. We integrate PGS associations from seven studies in four countries (N = 1,197,129) with disease incidences from the Global Burden of Disease. PGS has a significant sex-specific effect for asthma, hip osteoarthritis, gout, coronary heart disease and type 2 diabetes (T2D), with all but T2D exhibiting a larger effect in men. PGS has a larger effect in younger individuals for 13 diseases, with effects decreasing linearly with age. We show for breast cancer that, relative to individuals in the bottom 20% of polygenic risk, the top 5% attain an absolute risk for screening eligibility 16.3 years earlier. Our framework increases the generalizability of results from biobank studies and the accuracy of absolute risk estimates by appropriately accounting for age- and sex-specific PGS effects. Our results highlight the potential of PGS as a screening tool which may assist in the early prevention of common diseases.
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Predisposição Genética para Doença , Herança Multifatorial , Humanos , Masculino , Feminino , Herança Multifatorial/genética , Incidência , Pessoa de Meia-Idade , Adulto , Idoso , Diabetes Mellitus Tipo 2/genética , Diabetes Mellitus Tipo 2/epidemiologia , Fatores de Risco , Medição de Risco/métodos , Carga Global da Doença , Fatores Sexuais , Fatores EtáriosRESUMO
BACKGROUND: Adjustment for race is discouraged in lung-function testing, but the implications of adopting race-neutral equations have not been comprehensively quantified. METHODS: We obtained longitudinal data from 369,077 participants in the National Health and Nutrition Examination Survey, U.K. Biobank, the Multi-Ethnic Study of Atherosclerosis, and the Organ Procurement and Transplantation Network. Using these data, we compared the race-based 2012 Global Lung Function Initiative (GLI-2012) equations with race-neutral equations introduced in 2022 (GLI-Global). Evaluated outcomes included national projections of clinical, occupational, and financial reclassifications; individual lung-allocation scores for transplantation priority; and concordance statistics (C statistics) for clinical prediction tasks. RESULTS: Among the 249 million persons in the United States between 6 and 79 years of age who are able to produce high-quality spirometric results, the use of GLI-Global equations may reclassify ventilatory impairment for 12.5 million persons, medical impairment ratings for 8.16 million, occupational eligibility for 2.28 million, grading of chronic obstructive pulmonary disease for 2.05 million, and military disability compensation for 413,000. These potential changes differed according to race; for example, classifications of nonobstructive ventilatory impairment may change dramatically, increasing 141% (95% confidence interval [CI], 113 to 169) among Black persons and decreasing 69% (95% CI, 63 to 74) among White persons. Annual disability payments may increase by more than $1 billion among Black veterans and decrease by $0.5 billion among White veterans. GLI-2012 and GLI-Global equations had similar discriminative accuracy with regard to respiratory symptoms, health care utilization, new-onset disease, death from any cause, death related to respiratory disease, and death among persons on a transplant waiting list, with differences in C statistics ranging from -0.008 to 0.011. CONCLUSIONS: The use of race-based and race-neutral equations generated similarly accurate predictions of respiratory outcomes but assigned different disease classifications, occupational eligibility, and disability compensation for millions of persons, with effects diverging according to race. (Funded by the National Heart Lung and Blood Institute and the National Institute of Environmental Health Sciences.).
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Testes de Função Respiratória , Insuficiência Respiratória , Adolescente , Adulto , Idoso , Criança , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Adulto Jovem , Pneumopatias/diagnóstico , Pneumopatias/economia , Pneumopatias/etnologia , Pneumopatias/terapia , Transplante de Pulmão/estatística & dados numéricos , Inquéritos Nutricionais/estatística & dados numéricos , Doença Pulmonar Obstrutiva Crônica/diagnóstico , Doença Pulmonar Obstrutiva Crônica/economia , Doença Pulmonar Obstrutiva Crônica/etnologia , Doença Pulmonar Obstrutiva Crônica/terapia , Grupos Raciais , Testes de Função Respiratória/classificação , Testes de Função Respiratória/economia , Testes de Função Respiratória/normas , Espirometria , Estados Unidos/epidemiologia , Insuficiência Respiratória/diagnóstico , Insuficiência Respiratória/economia , Insuficiência Respiratória/etnologia , Insuficiência Respiratória/terapia , Negro ou Afro-Americano/estatística & dados numéricos , Brancos/estatística & dados numéricos , Avaliação da Deficiência , Ajuda a Veteranos de Guerra com Deficiência/classificação , Ajuda a Veteranos de Guerra com Deficiência/economia , Ajuda a Veteranos de Guerra com Deficiência/estatística & dados numéricos , Pessoas com Deficiência/classificação , Pessoas com Deficiência/estatística & dados numéricos , Doenças Profissionais/diagnóstico , Doenças Profissionais/economia , Doenças Profissionais/etnologia , Financiamento Governamental/economia , Financiamento Governamental/estatística & dados numéricosRESUMO
Underrepresented populations are often excluded from genomic studies owing in part to a lack of resources supporting their analyses. The 1000 Genomes Project (1kGP) and Human Genome Diversity Project (HGDP), which have recently been sequenced to high coverage, are valuable genomic resources because of the global diversity they capture and their open data sharing policies. Here, we harmonized a high-quality set of 4094 whole genomes from 80 populations in the HGDP and 1kGP with data from the Genome Aggregation Database (gnomAD) and identified over 153 million high-quality SNVs, indels, and SVs. We performed a detailed ancestry analysis of this cohort, characterizing population structure and patterns of admixture across populations, analyzing site frequency spectra, and measuring variant counts at global and subcontinental levels. We also show substantial added value from this data set compared with the prior versions of the component resources, typically combined via liftOver and variant intersection; for example, we catalog millions of new genetic variants, mostly rare, compared with previous releases. In addition to unrestricted individual-level public release, we provide detailed tutorials for conducting many of the most common quality-control steps and analyses with these data in a scalable cloud-computing environment and publicly release this new phased joint callset for use as a haplotype resource in phasing and imputation pipelines. This jointly called reference panel will serve as a key resource to support research of diverse ancestry populations.
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Bases de Dados Genéticas , Genoma Humano , Humanos , Projeto Genoma Humano , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Variação Genética , Genômica/métodosRESUMO
Genome-wide association studies (GWAS) have been predominantly conducted in populations of European ancestry, limiting opportunities for biological discovery in diverse populations. We report GWAS findings from 153,950 individuals across 36 quantitative traits in the Korean Cancer Prevention Study-II (KCPS2) Biobank. We discovered 616 novel genetic loci in KCPS2, including an association between thyroid-stimulating hormone and CD36. Meta-analysis with the Korean Genome and Epidemiology Study, Biobank Japan, Taiwan Biobank, and UK Biobank identified 3,524 loci that were not significant in any contributing GWAS. We describe differences in genetic architectures across these East Asian and European samples. We also highlight East Asian specific associations, including a known pleiotropic missense variant in ALDH2, which fine-mapping identified as a likely causal variant for a diverse set of traits. Our findings provide insights into the genetic architecture of complex traits in East Asian populations and highlight how broadening the population diversity of GWAS samples can aid discovery.
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Advancements in genomic technologies have shown remarkable promise for improving health trajectories. The Human Genome Project has catalyzed the integration of genomic tools into clinical practice, such as disease risk assessment, prenatal testing and reproductive genomics, cancer diagnostics and prognostication, and therapeutic decision making. Despite the promise of genomic technologies, their full potential remains untapped without including individuals of diverse ancestries and integrating social determinants of health (SDOHs). The NHGRI launched the 2020 Strategic Vision with ten bold predictions by 2030, including "individuals from ancestrally diverse backgrounds will benefit equitably from advances in human genomics." Meeting this goal requires a holistic approach that brings together genomic advancements with careful consideration to healthcare access as well as SDOHs to ensure that translation of genetics research is inclusive, affordable, and accessible and ultimately narrows rather than widens health disparities. With this prediction in mind, this review delves into the two paramount applications of genetic testing-reproductive genomics and precision oncology. When discussing these applications of genomic advancements, we evaluate current accessibility limitations, highlight challenges in achieving representativeness, and propose paths forward to realize the ultimate goal of their equitable applications.
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Genômica , Medicina de Precisão , Humanos , Genômica/métodos , Medicina de Precisão/métodos , Genoma Humano , Testes Genéticos , Neoplasias/genética , Acessibilidade aos Serviços de SaúdeRESUMO
Genomic scientists have long been promised cheaper DNA sequencing, but deep whole genomes are still costly, especially when considered for large cohorts in population-level studies. More affordable options include microarrays + imputation, whole exome sequencing (WES), or low-pass whole genome sequencing (WGS) + imputation. WES + array + imputation has recently been shown to yield 99% of association signals detected by WGS. However, a method free from ascertainment biases of arrays or the need for merging different data types that still benefits from deeper exome coverage to enhance novel coding variant detection does not exist. We developed a new, combined, "Blended Genome Exome" (BGE) in which a whole genome library is generated, an aliquot of that genome is amplified by PCR, the exome regions are selected and enriched, and the genome and exome libraries are combined back into a single tube for sequencing (33% exome, 67% genome). This creates a single CRAM with a low-coverage whole genome (2-3x) combined with a higher coverage exome (30-40x). This BGE can be used for imputing common variants throughout the genome as well as for calling rare coding variants. We tested this new method and observed >99% r 2 concordance between imputed BGE data and existing 30x WGS data for exome and genome variants. BGE can serve as a useful and cost-efficient alternative sequencing product for genomic researchers, requiring ten-fold less sequencing compared to 30x WGS without the need for complicated harmonization of array and sequencing data.
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The quality improvement study examines the use of risk-adaptive adjuvant radiotherapy in women with nonmismatch repair deficiency endometrial cancer.
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Neoplasias do Endométrio , Estadiamento de Neoplasias , Humanos , Feminino , Neoplasias do Endométrio/radioterapia , Neoplasias do Endométrio/patologia , Radioterapia Adjuvante , Uso Excessivo dos Serviços de Saúde , Pessoa de Meia-Idade , Idoso , SubtratamentoRESUMO
Polygenic risk scores (PRSs) are an emerging tool to predict the clinical phenotypes and outcomes of individuals. We propose PRSmix, a framework that leverages the PRS corpus of a target trait to improve prediction accuracy, and PRSmix+, which incorporates genetically correlated traits to better capture the human genetic architecture for 47 and 32 diseases/traits in European and South Asian ancestries, respectively. PRSmix demonstrated a mean prediction accuracy improvement of 1.20-fold (95% confidence interval [CI], [1.10; 1.3]; p = 9.17 × 10-5) and 1.19-fold (95% CI, [1.11; 1.27]; p = 1.92 × 10-6), and PRSmix+ improved the prediction accuracy by 1.72-fold (95% CI, [1.40; 2.04]; p = 7.58 × 10-6) and 1.42-fold (95% CI, [1.25; 1.59]; p = 8.01 × 10-7) in European and South Asian ancestries, respectively. Compared to the previously cross-trait-combination methods with scores from pre-defined correlated traits, we demonstrated that our method improved prediction accuracy for coronary artery disease up to 3.27-fold (95% CI, [2.1; 4.44]; p value after false discovery rate (FDR) correction = 2.6 × 10-4). Our method provides a comprehensive framework to benchmark and leverage the combined power of PRS for maximal performance in a desired target population.
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Doença da Artéria Coronariana , Osteopatia , Humanos , Herança Multifatorial/genética , Estratificação de Risco Genético , Benchmarking , Doença da Artéria Coronariana/diagnósticoRESUMO
Primary open-angle glaucoma (POAG), a leading cause of irreversible blindness globally, shows disparity in prevalence and manifestations across ancestries. We perform meta-analysis across 15 biobanks (of the Global Biobank Meta-analysis Initiative) (n = 1,487,441: cases = 26,848) and merge with previous multi-ancestry studies, with the combined dataset representing the largest and most diverse POAG study to date (n = 1,478,037: cases = 46,325) and identify 17 novel significant loci, 5 of which were ancestry specific. Gene-enrichment and transcriptome-wide association analyses implicate vascular and cancer genes, a fifth of which are primary ciliary related. We perform an extensive statistical analysis of SIX6 and CDKN2B-AS1 loci in human GTEx data and across large electronic health records showing interaction between SIX6 gene and causal variants in the chr9p21.3 locus, with expression effect on CDKN2A/B. Our results suggest that some POAG risk variants may be ancestry specific, sex specific, or both, and support the contribution of genes involved in programmed cell death in POAG pathogenesis.
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Predisposição Genética para Doença , Glaucoma de Ângulo Aberto , Masculino , Feminino , Humanos , Predisposição Genética para Doença/genética , Glaucoma de Ângulo Aberto/genética , Glaucoma de Ângulo Aberto/epidemiologia , Polimorfismo de Nucleotídeo Único , Proliferação de Células , BiologiaRESUMO
Underrepresented populations are often excluded from genomic studies due in part to a lack of resources supporting their analyses. The 1000 Genomes Project (1kGP) and Human Genome Diversity Project (HGDP), which have recently been sequenced to high coverage, are valuable genomic resources because of the global diversity they capture and their open data sharing policies. Here, we harmonized a high quality set of 4,094 whole genomes from HGDP and 1kGP with data from the Genome Aggregation Database (gnomAD) and identified over 153 million high-quality SNVs, indels, and SVs. We performed a detailed ancestry analysis of this cohort, characterizing population structure and patterns of admixture across populations, analyzing site frequency spectra, and measuring variant counts at global and subcontinental levels. We also demonstrate substantial added value from this dataset compared to the prior versions of the component resources, typically combined via liftover and variant intersection; for example, we catalog millions of new genetic variants, mostly rare, compared to previous releases. In addition to unrestricted individual-level public release, we provide detailed tutorials for conducting many of the most common quality control steps and analyses with these data in a scalable cloud-computing environment and publicly release this new phased joint callset for use as a haplotype resource in phasing and imputation pipelines. This jointly called reference panel will serve as a key resource to support research of diverse ancestry populations.
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The depletion of disruptive variation caused by purifying natural selection (constraint) has been widely used to investigate protein-coding genes underlying human disorders1-4, but attempts to assess constraint for non-protein-coding regions have proved more difficult. Here we aggregate, process and release a dataset of 76,156 human genomes from the Genome Aggregation Database (gnomAD)-the largest public open-access human genome allele frequency reference dataset-and use it to build a genomic constraint map for the whole genome (genomic non-coding constraint of haploinsufficient variation (Gnocchi)). We present a refined mutational model that incorporates local sequence context and regional genomic features to detect depletions of variation. As expected, the average constraint for protein-coding sequences is stronger than that for non-coding regions. Within the non-coding genome, constrained regions are enriched for known regulatory elements and variants that are implicated in complex human diseases and traits, facilitating the triangulation of biological annotation, disease association and natural selection to non-coding DNA analysis. More constrained regulatory elements tend to regulate more constrained protein-coding genes, which in turn suggests that non-coding constraint can aid the identification of constrained genes that are as yet unrecognized by current gene constraint metrics. We demonstrate that this genome-wide constraint map improves the identification and interpretation of functional human genetic variation.
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Genoma Humano , Genômica , Modelos Genéticos , Mutação , Humanos , Acesso à Informação , Bases de Dados Genéticas , Conjuntos de Dados como Assunto , Frequência do Gene , Genoma Humano/genética , Mutação/genética , Seleção GenéticaRESUMO
Smoking is the leading risk factor for chronic obstructive pulmonary disease (COPD) worldwide, yet many people who never smoke develop COPD. We perform a longitudinal analysis of COPD in the UK Biobank to derive and validate the Socioeconomic and Environmental Risk Score which captures additive and cumulative environmental, behavioral, and socioeconomic exposure risks beyond tobacco smoking. The Socioeconomic and Environmental Risk Score is more predictive of COPD than smoking status and pack-years. Individuals in the highest decile of the risk score have a greater risk for incident COPD compared to the remaining population. Never smokers in the highest decile of exposure risk are more likely to develop COPD than previous and current smokers in the lowest decile. In general, the prediction accuracy of the Social and Environmental Risk Score is lower in non-European populations. While smoking status is often considered in screening COPD, our finding highlights the importance of other non-smoking environmental and socioeconomic variables.
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Doença Pulmonar Obstrutiva Crônica , Humanos , Doença Pulmonar Obstrutiva Crônica/epidemiologia , Doença Pulmonar Obstrutiva Crônica/etiologia , Fatores de Risco , Fumar/efeitos adversos , Fumar/epidemiologiaRESUMO
Mass General Brigham, an integrated healthcare system based in the Greater Boston area of Massachusetts, annually serves 1.5 million patients. We established the Mass General Brigham Biobank (MGBB), encompassing 142,238 participants, to unravel the intricate relationships among genomic profiles, environmental context, and disease manifestations within clinical practice. In this study, we highlight the impact of ancestral diversity in the MGBB by employing population genetics, geospatial assessment, and association analyses of rare and common genetic variants. The population structures captured by the genetics mirror the sequential immigration to the Greater Boston area throughout American history, highlighting communities tied to shared genetic and environmental factors. Our investigation underscores the potency of unbiased, large-scale analyses in a healthcare-affiliated biobank, elucidating the dynamic interplay across genetics, immigration, structural geospatial factors, and health outcomes in one of the earliest American sites of European colonization.
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Polygenic risk scores (PRSs) developed from multi-ancestry genome-wide association studies (GWASs), PRSmulti, hold promise for improving PRS accuracy and generalizability across populations. To establish best practices for leveraging the increasing diversity of genomic studies, we investigated how various factors affect the performance of PRSmulti compared with PRSs constructed from single-ancestry GWASs (PRSsingle). Through extensive simulations and empirical analyses, we showed that PRSmulti overall outperformed PRSsingle in understudied populations, except when the understudied population represented a small proportion of the multi-ancestry GWAS. Furthermore, integrating PRSs based on local ancestry-informed GWASs and large-scale, European-based PRSs improved predictive performance in understudied African populations, especially for less polygenic traits with large-effect ancestry-enriched variants. Our work highlights the importance of diversifying genomic studies to achieve equitable PRS performance across ancestral populations and provides guidance for developing PRSs from multiple studies.
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Smoking is the leading risk factor for chronic obstructive pulmonary disease (COPD) worldwide, yet many people who never smoke develop COPD. We hypothesize that considering other socioeconomic and environmental factors can better predict and stratify the risk of COPD in both non-smokers and smokers. We performed longitudinal analysis of COPD in the UK Biobank to develop the Socioeconomic and Environmental Risk Score (SERS) which captures additive and cumulative environmental, behavioral, and socioeconomic exposure risks beyond tobacco smoking. We tested the ability of SERS to predict and stratify the risk of COPD in current, previous, and never smokers of European and non-European ancestries in comparison to a composite genome-wide polygenic risk score (PGS). We tested associations using Cox regression models and assessed the predictive performance of models using Harrell's C index. SERS (C index = 0.770, 95% CI 0.756 to 0.784) was more predictive of COPD than smoking status (C index = 0.738, 95% CI 0.724 to 0.752), pack-years (C index = 0.742, 95% CI 0.727 to 0.756). Compared to the remaining population, individuals in the highest decile of the SERS had hazard ratios (HR) = 7.24 (95% CI 6.51 to 8.05, P < 0.0001) for incident COPD. Never smokers in the highest decile of exposure risk were more likely to develop COPD than previous and current smokers in the lowest decile with HR=4.95 (95% CI 1.56 to 15.69, P=6.65×10-3) and 2.92 (95%CI 1.51 to 5.61, P=1.38×10-3), respectively. In general, the prediction accuracy of SERS was lower in the non-European populations compared to the European evaluation set. In addition to genetic factors, socioeconomic and environmental factors beyond smoking can predict and stratify COPD risk for both non- and smoking individuals. Smoking status is often considered in screening; other non-smoking environmental and non-genetic variables should be evaluated prospectively for their clinical utility.
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In this consensus report by a diverse group of academics who conduct and/or are concerned about social and behavioral genomics (SBG) research, the authors recount the often-ugly history of scientific attempts to understand the genetic contributions to human behaviors and social outcomes. They then describe what the current science-including genomewide association studies and polygenic indexes-can and cannot tell us, as well as its risks and potential benefits. They conclude with a discussion of responsible behavior in the context of SBG research. SBG research that compares individuals within a group according to a "sensitive" phenotype requires extra attention to responsible conduct and to responsible communication about the research and its findings. SBG research (1) on sensitive phenotypes that (2) compares two or more groups defined by (a) race, (b) ethnicity, or (c) genetic ancestry (where genetic ancestry could easily be misunderstood as race or ethnicity) requires a compelling justification to be conducted, funded, or published. All authors agree that this justification at least requires a convincing argument that a study's design could yield scientifically valid results; some authors would additionally require the study to have a socially favorable risk-benefit profile.