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We explored the role of genetic ancestry in shaping the genetic architecture of whole blood gene expression using whole genome and RNA sequencing data from 2,733 African American and Hispanic/Latino children. We find that heritability of gene expression significantly increases with greater proportion of genome-wide African ancestry and decreases with higher levels of Indigenous American ancestry. Fine-mapping of expression quantitative trait loci (eQTLs) in individuals with predominantly African or Indigenous American ancestry revealed ancestry-specific eQTLs in over 30% of heritable genes. We leveraged our data to train genetically derived transcriptome prediction models, which identified significantly more associated genes than GTEx or MESA models when applied to 28 traits from a multi-ancestry population. Our findings underscore the importance of increasing representation from ancestrally diverse populations in genomic studies to enable new discoveries and ensure their equitable translation., {"references":["Kachuri L, Mak ACY, Hu D et al. (2022) Gene expression in African Americans and Latinos reveals ancestry-specific patterns of genetic architecture. bioRxiv. doi:10.1101/2021.08.19.456901"]}

One mechanism by which genetic factors influence complex traits and diseases is altering gene expression. Direct measurement of gene expression in relevant tissues is rarely tenable; however, genetically regulated gene expression (GReX) can be estimated using prediction models derived from large multi-omic datasets. These approaches have led to the discovery of many gene-trait associations, but whether models derived from predominantly European ancestry (EA) reference panels can map novel associations in ancestrally diverse populations remains unclear. We applied PrediXcan to impute GReX in 51,520 ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) participants (35% African American, 45% Hispanic/Latino, 10% Asian, and 7% Hawaiian) across 25 key cardiometabolic traits and relevant tissues to identify 102 novel associations. We then compared associations in PAGE to those in a random subset of 50,000 White British participants from UK Biobank (UKBB(50k)) for height and body mass index (BMI). We identified 517 associations across 47 tissues in PAGE but not UKBB(50k), demonstrating the importance of diverse samples in identifying trait-associated GReX. We observed that variants used in PrediXcan models were either more or less differentiated across continental-level populations than matched-control variants depending on the specific population reflecting sampling bias. Additionally, variants from identified genes specific to either PAGE or UKBB(50k) analyses were more ancestrally differentiated than those in genes detected in both analyses, underlining the value of population-specific discoveries. This suggests that while EA-derived transcriptome imputation models can identify new associations in non-EA populations, models derived from closely matched reference panels may yield further insights. Our findings call for more diversity in reference datasets of tissue-specific gene expression.

Peripheral artery disease (PAD) is a form of atherosclerotic cardiovascular disease, affecting ∼8 million Americans, and is known to have racial and ethnic disparities. PAD has been reported to have significantly higher prevalence in African Americans (AAs) compared to non-Hispanic European Americans (EAs). Hispanic/Latinos (HLs) have been reported to have lower or similar rates of PAD compared to EAs, despite having a paradoxically high burden of PAD risk factors, however recent work suggests prevalence may differ between sub-groups. Here we examined a large cohort of diverse adults in the BioMebiobank in New York City (NYC). We observed the prevalence of PAD at 1.7% in EAs vs 8.5% and 9.4% in AAs and HLs, respectively; and among HL sub-groups, at 11.4% and 11.5% in Puerto Rican and Dominican populations, respectively. Follow-up analysis that adjusted for common risk factors demonstrated that Dominicans had the highest increased risk for PAD relative to EAs (OR=3.15 (95% CI 2.33-4.25),P-14). To investigate whether genetic factors may explain this increased risk, we performed admixture mapping by testing the association between local ancestry (LA) and PAD in Dominican BioMeparticipants (N=1,940) separately for European (EUR), African (AFR) and Native American (NAT) continental ancestry tracts. We identified a NAT ancestry tract at chromosome 2q35 that was significantly associated with PAD (OR=2.05 (95% CI 1.51-2.78),P-6) with 22.5% vs 12.5% PAD prevalence in heterozygous NAT tract carriers versus non-carriers, respectively. Fine-mapping at this locus implicated tag SNP rs78529201 located within a long intergenic non-coding RNA (lincRNA)LINC00607, a gene expression regulator of key genes related to thrombosis and extracellular remodeling of endothelial cells, suggesting a putative link of the 2q35 locus to PAD etiology. In summary, we showed how leveraging health systems data helped understand nuances of PAD risk across HL sub-groups and admixture mapping approaches elucidated a novel risk locus in a Dominican population.

Inadequate representation of non-European ancestry populations in genome-wide association studies (GWAS) has limited opportunities to isolate functional variants. Fine-mapping in multi-ancestry populations should improve the efficiency of prioritizing variants for functional interrogation. To evaluate this hypothesis, we leveraged ancestry architecture to perform comparative GWAS and fine-mapping of obesity related phenotypes in European ancestry populations from the UK Biobank (UKBB) and multi-ancestry samples from the Population Architecture for Genetic Epidemiology (PAGE) consortium with comparable sample sizes. In 10 of the investigated regions with genome wide significant associations for obesity related traits, fine-mapping in our ancestrally diverse sample led to 95% and 99% credible sets (CS) with fewer variants than in the European ancestry sample. Lead fine-mapped variants in PAGE regions had higher average coding scores, and higher average posterior probabilities for causality compared to UKBB. Importantly, 99% CS in PAGE loci contained strong expression quantitative trait loci (eQTLs) in adipose tissues or harbored more variants in tighter linkage disequilibrium (LD) with eQTLs. Results also suggested three novel candidates for functional effect on waist-to-hip ratio adjusted for BMI (WHRBMI-adj) (rs5781117 near gene RP11-392O17.1, rs10187501 in gene COBLL1, and rs1964599 near gene CCDC92), all within the 99% CS. Leveraging ancestrally diverse populations with heterogeneous ancestry architectures, coupled with functional annotation, increased fine mapping efficiency and performance, and reduced the set of candidate variants for consideration for future functional studies. Significant overlap in genetic causal variants across populations suggest generalizability of genetic mechanisms underpinning obesity related traits across populations.

Over 6.37 million people have died from COVID-19 worldwide, but factors influencing COVID-19-related mortality remain understudied. We aimed to describe and identify risk factors for COVID-19 mortality in the Colorado Center for Personalized Medicine (CCPM) Biobank using integrated data sources, including Electronic Health Records (EHRs). We calculated cause-specific mortality and case-fatality rates for COVID-19 and common pre-existing health conditions defined by diagnostic phecodes and encounters in EHRs. We performed multivariable logistic regression analyses of the association between each pre-existing condition and COVID-19 mortality. Of the 155,859 Biobank participants enrolled as of July 2022, 20,797 had been diagnosed with COVID-19. Of 5334 Biobank participants who had died, 190 were attributed to COVID-19. The case-fatality rate was 0.91% and the COVID-19 mortality rate was 122 per 100,000 persons. The odds of dying from COVID-19 were significantly increased among older men, and those with 14 of the 61 pre-existing conditions tested, including hypertensive chronic kidney disease (OR: 10.14, 95% CI: 5.48, 19.16) and type 2 diabetes with renal manifestations (OR: 5.59, 95% CI: 3.42, 8.97). Male patients who are older and have pre-existing kidney diseases may be at higher risk for death from COVID-19 and may require special care.

Groups of distantly related individuals who share a short segment of their genome identical-by-descent (IBD) can provide insights about rare traits and diseases in massive biobanks using IBD mapping. Clustering algorithms play an important role in finding these groups accurately and at scale. We set out to analyze the fitness of commonly used, fast and scalable clustering algorithms for IBD mapping applications. We designed a realistic benchmark for local IBD graphs and utilized it to compare the statistical power of clustering algorithms via simulating 2.3 million clusters across 850 experiments. We found Infomap and Markov Clustering (MCL) community detection methods to have high statistical power in most of the scenarios. They yield a 30% increase in power compared to the current state-of-art approach, with a 3 orders of magnitude lower runtime. We also found that standard clustering metrics, such as modularity, cannot predict statistical power of algorithms in IBD mapping applications. We extend our findings to real datasets by analyzing the Population Architecture using Genomics and Epidemiology (PAGE) Study dataset with 51,000 samples and 2 million shared segments on Chromosome 1, resulting in the extraction of 39 million local IBD clusters. We demonstrate the power of our approach by recovering signals of rare genetic variation in the Whole-Exome Sequence data of 200,000 individuals in the UK Biobank. We provide an efficient implementation to enable clustering at scale for IBD mapping for various populations and scenarios.Supplementary Information: The code, along with supplementary methods and figures are available at https://github.com/roohy/localIBDClustering.

Funding Information: We acknowledge all the participants and researchers of the 23 biobanks that have contributed to the GBMI. Biobank-specific acknowledgments are included in the Data S3 . We thank H. Huang, A.R. Martin, B.M. Neale, Y. Okada, K. Tsuo, J.C. Ulirsch, Y. Wang, and all the members of Finucane and Daly labs for their helpful feedback. M.K. was supported by a Nakajima Foundation Fellowship and the Masason Foundation . H.K.F. was funded by NIH grant DP5 OD024582 . Publisher Copyright: © 2022 The Author(s) Meta-analysis is pervasively used to combine multiple genome-wide association studies (GWASs). Fine-mapping of meta-analysis studies is typically performed as in a single-cohort study. Here, we first demonstrate that heterogeneity (e.g., of sample size, phenotyping, imputation) hurts calibration of meta-analysis fine-mapping. We propose a summary statistics-based quality-control (QC) method, suspicious loci analysis of meta-analysis summary statistics (SLALOM), that identifies suspicious loci for meta-analysis fine-mapping by detecting outliers in association statistics. We validate SLALOM in simulations and the GWAS Catalog. Applying SLALOM to 14 meta-analyses from the Global Biobank Meta-analysis Initiative (GBMI), we find that 67% of loci show suspicious patterns that call into question fine-mapping accuracy. These predicted suspicious loci are significantly depleted for having nonsynonymous variants as lead variant (2.7×; Fisher's exact p = 7.3 × 10−4). We find limited evidence of fine-mapping improvement in the GBMI meta-analyses compared with individual biobanks. We urge extreme caution when interpreting fine-mapping results from meta-analysis of heterogeneous cohorts.

Inflammatory Bowel Disease (IBD) is characterized by complex etiology and a disrupted gut microbiota. The substantial non-genetic variance for Crohn’s disease and ulcerative colitis (≥25%) suggests that both genetic and environmental factors contribute to IBD development. We aim to extend the framework of genomic studies by examining gut characteristics that are affected by both genetic and environmental factors. Specifically, we train models and validate their accuracy using data that quantifies the microbiota, their transcripts, and the metabolites present in the gut. The IBD Multi-omics Database from the Human Microbiome Project 2 provided 1,785 repeated samples for 131 individuals (103 cases, 27 controls) across multiple -omics layers including metagenomics, metatranscriptomics, viromics, and metabolomics. After splitting the subjects into training and validation groups, we used mixed effects least absolute shrinkage and selection operator (LASSO) regression to determine the most relevant features for each -omic layer. These features, along with demographic covariates, were incorporated into a polygenic risk score framework to generate four separate -omic-level prediction scores. All four -omic-level scores were then combined into a final regression to assess the relative importance of individual -omics and the added benefits when considered together. Our models identified several species, pathways, and metabolites known to be associated with IBD risk. Individually, metabolomics and viromics based scores were more predictive than metagenomics or metatranscriptomics based scores, and when all four scores were combined, we predicted disease diagnosis with a Nagelkerke’s R2 of 0.46 and an AUC of 0.80 [95% CI: 0.63, 0.98].ImportanceThe health burden of inflammatory bowel disease (IBD) among affected individuals is large, and its complex etiology has been studied using high throughput -omics technology. We applied a prediction framework across multiple -omics from the gut microbiome (metagenomics, metatranscriptomics, metabolomics, and viromics) to predict diagnoses of Crohn’s disease and ulcerative colitis. The predicted scores from our models illustrated key features and allowed us to compare the relative utility of each -omic data type when used individually versus when combined in a multi-omics model. The individual and combined models performed well and emphasized the importance of metabolomics and viromics over metagenomics and metatranscriptomics predictive scores for IBD. The better predictive capability of metabolomics and viromics is likely because these -omics also serve as markers of lifestyle factors such as diet. This study shows the utility of combining multiple -omic data types to disentangle complex disease etiologies and biological signatures.

Summary statistics from genome-wide association studies (GWAS) represent a huge potential for research. A challenge for researchers in this field is the access and sharing of summary statistics data due to a lack of standards for the data content and file format. For this reason, the GWAS Catalog hosted a series of meetings in 2021 with summary statistics stakeholders to guide the development of a standard format. The key requirements from the stakeholders were for a standard that contained key data elements to be able to support a wide range of data analyses, required low bioinformatics skills for file access and generation, to have easily accessible metadata, and unambiguous and interoperable data. Here, we define the specifications for the first version of the GWAS-SSF format, which was developed to meet the requirements discussed with the community. GWAS-SSF consists of a tab-separated data file with well-defined fields and an accompanying metadata file.

Latin America continues to be severely underrepresented in genomics research, and fine-scale genetic histories as well as complex trait architectures remain hidden due to the lack of Big Data. To fill this gap, the Mexican Biobank project genotyped 1.8 million markers in 6,057 individuals from 32 states and 898 sampling localities across Mexico with linked complex trait and disease information creating a valuable nationwide genotype-phenotype database. Through a suite of state-of-the-art methods for ancestry deconvolution and inference of identity-by-descent (IBD) segments, we inferred detailed ancestral histories for the last 200 generations in different Mesoamerican regions, unraveling native and colonial/post-colonial demographic dynamics. We observed large variations in runs of homozygosity (ROH) among genomic regions with different ancestral origins reflecting their demographic histories, which also affect the distribution of rare deleterious variants across Mexico. We analyzed a range of biomedical complex traits and identified significant genetic and environmental factors explaining their variation, such as ROH found to be significant predictors for trait variation in BMI and triglycerides.

Precision medicine initiatives across the globe have led to a revolution of repositories linking large-scale genomic data with electronic health records, enabling genomic analyses across the entire phenome. Many of these initiatives focus solely on research insights, leading to limited direct benefit to patients. We describe the Biobank at the Colorado Center for Personalized Medicine (CCPM Biobank) that was jointly developed by the University of Colorado Anschutz Medical Campus and UCHealth to serve as a unique, dual-purpose research and clinical resource accelerating personalized medicine. This living resource currently has over 200,000 patients with ongoing recruitment. We highlight the clinical, laboratory, regulatory, and HIPAA-compliant informatics infrastructure along with our stakeholder engagement, consent, recontact, and participant engagement strategies. We characterize aspects of genetic and geographic diversity unique to the Rocky Mountain Region, the primary catchment area for CCPM Biobank participants. We leverage linked health and demographic information of the CCPM Biobank participant population to demonstrate the utility of the CCPM Biobank to replicate complex trait associations in the first 33,674 genotyped patients across multiple disease domains. Finally, we describe our current efforts towards return of clinical genetic test results including high-impact pathogenic variants and pharmacogenetic information, and our broader goals as the CCPM Biobank continues to grow. Bringing clinical and research interests together fosters unique clinical and translational questions that can be addressed from the large EHR-linked CCPM Biobank resource within a HIPAA and CLIA-certified environment.

Preeclampsia is a multi-organ complication of pregnancy characterized by sudden hypertension and proteinuria that is among the leading causes of preterm delivery and maternal morbidity and mortality worldwide. The heterogeneity of preeclampsia poses a challenge for understanding its etiology and molecular basis. Intriguingly, risk for the condition increases in high-altitude regions such as the Peruvian Andes. To investigate the genetic basis of preeclampsia in a population living at high altitude, we characterized genome-wide variation in a cohort of preeclamptic and healthy Andean families (n = 883) from Puno, Peru, a city located above 3,800 meters of altitude. Our study collected genomic DNA and medical records from case-control trios and duos in local hospital settings. We generated genotype data for 439,314 SNPs, determined global ancestry patterns, and mapped associations between genetic variants and preeclampsia phenotypes. A transmission disequilibrium test (TDT) revealed variants near genes of biological importance for placental and blood vessel function. The top candidate region was found on chromosome 13 of the fetal genome and contains clotting factor genes PROZ, F7, and F10. These findings provide supporting evidence that common genetic variants within coagulation genes play an important role in preeclampsia. A selection scan revealed a potential adaptive signal around the ADAM12 locus on chromosome 10, implicated in pregnancy disorders. Our discovery of an association in a functional pathway relevant to pregnancy physiology in an understudied population of Native American origin demonstrates the increased power of family-based study design and underscores the importance of conducting genetic research in diverse populations.

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

Human natural killer (NK) cells are essential for controlling infection, cancer, and fetal development. NK cell functions are modulated by interactions between polymorphic inhibitory killer cell immunoglobulin-like receptors (KIR) and polymorphic HLA-A, -B, and -C ligands expressed on tissue cells. All HLA-C alleles encode a KIR ligand and contribute to reproduction and immunity. In contrast, only some HLA-A and -B alleles encode KIR ligands and they focus on immunity. By high-resolution analysis of KIR and HLA-A, -B, and -C genes, we show that the Chinese Southern Han (CHS) are significantly enriched for interactions between inhibitory KIR and HLA-A and -B. This enrichment has had substantial input through population admixture with neighboring populations, who contributed HLA class I haplotypes expressing the KIR ligands B*46:01 and B*58:01, which subsequently rose to high frequency by natural selection. Consequently, over 80% of Southern Han HLA haplotypes encode more than one KIR ligand. Complementing the high number of KIR ligands, the CHS KIR locus combines a high frequency of genes expressing potent inhibitory KIR, with a low frequency of those expressing activating KIR. The Southern Han centromeric KIR region encodes strong, conserved, inhibitory HLA-C-specific receptors, and the telomeric region provides a high number and diversity of inhibitory HLA-A and -B-specific receptors. In all these characteristics, the CHS represent other East Asians, whose NK cell repertoires are thus enhanced in quantity, diversity, and effector strength, likely augmenting resistance to endemic viral infections.

Polygenic risk scores (PRSs) have been widely explored in precision medicine. However, few studies have thoroughly investigated their best practices in global populations across different diseases. We here utilized data from Global Biobank Meta-analysis Initiative (GBMI) to explore methodological considerations and PRS performance in 9 different biobanks for 14 disease endpoints. Specifically, we constructed PRSs using pruning and thresholding (P + T) and PRS-continuous shrinkage (CS). For both methods, using a European-based linkage disequilibrium (LD) reference panel resulted in comparable or higher prediction accuracy compared with several other non-European-based panels. PRS-CS overall outperformed the classic P + T method, especially for endpoints with higher SNP-based heritability. Notably, prediction accuracy is heterogeneous across endpoints, biobanks, and ancestries, especially for asthma, which has known variation in disease prevalence across populations. Overall, we provide lessons for PRS construction, evaluation, and interpretation using GBMI resources and highlight the importance of best practices for PRS in the biobank-scale genomics era.

BackgroundCharacterizing the experience and impact of the COVID-19 pandemic among various populations remains challenging due to the limitations inherent in common data sources such as the electronic health record (EHR) or convenience sample surveys.ObjectiveTo describe testing behaviors, symptoms, impact, vaccination status and case ascertainment during the COVID-19 pandemic using integrated data sources.MethodsIn summer 2020 and 2021, we surveyed participants enrolled in the Biobank at the Colorado Center for Personalized Medicine (CCPM, N = 180,599) about their experience with COVID-19. Prevalence of testing, symptoms, and the impacts of COVID-19 on employment, family life, and physical and mental health were calculated overall and by demographic categories. Using the Electronic Health Record (EHR), we compared COVID-19 case ascertainment and characteristics in the EHR versus the survey.ResultsOf the 25,063 survey respondents (13.9%), 42.5% had been tested for COVID-19 and of those, 12.8% tested positive. Nearly half of those tested had symptoms and/or had been exposed to someone who was infected. Young adults (18-29 years) and Hispanics were more likely to have positive tests compared to older adults and persons of other racial/ethnic groups. Mental health (54.6%) and family life (48.8%) were most negatively affected by the pandemic and more so among younger groups and women; negative impacts on employment were more commonly reported among Black respondents. After integration with EHR data up to the time of the survey completion, 4.0% of survey respondents (n=1,006) had discordant COVID-19 case status between the EHR and the survey. Using all longitudinal EHR and survey data, we identified 11,472 COVID-positive cases among Biobank participants (6.4%). In comparison to COVID-19 cases identified through the survey, EHR-identified cases were younger and more likely to be Hispanic.ConclusionsIntegrated data assets such as the Biobank at the CCPM are key resources for population health monitoring in response to public health emergencies, such as the COVID-19 pandemic.

BACKGROUND Characterizing the experience and impact of the COVID-19 pandemic among various populations remains challenging due to the limitations inherent in common data sources, such as electronic health records (EHRs) or cross-sectional surveys. OBJECTIVE This study aims to describe testing behaviors, symptoms, impact, vaccination status, and case ascertainment during the COVID-19 pandemic using integrated data sources. METHODS In summer 2020 and 2021, we surveyed participants enrolled in the Biobank at the Colorado Center for Personalized Medicine (CCPM; N=180,599) about their experience with COVID-19. The prevalence of testing, symptoms, and impacts of COVID-19 on employment, family life, and physical and mental health were calculated overall and by demographic categories. Survey respondents who reported receiving a positive COVID-19 test result were considered a “confirmed case” of COVID-19. Using EHRs, we compared COVID-19 case ascertainment and characteristics in EHRs versus the survey. Positive cases were identified in EHRs using the International Statistical Classification of Diseases, 10th revision (ICD-10) diagnosis codes, health care encounter types, and encounter primary diagnoses. RESULTS Of the 25,063 (13.9%) survey respondents, 10,661 (42.5%) had been tested for COVID-19, and of those, 1366 (12.8%) tested positive. Nearly half of those tested had symptoms or had been exposed to someone who was infected. Young adults (18-29 years) and Hispanics were more likely to have positive tests compared to older adults and persons of other racial/ethnic groups. Mental health (n=13,688, 54.6%) and family life (n=12,233, 48.8%) were most negatively affected by the pandemic and more so among younger groups and women; negative impacts on employment were more commonly reported among Black respondents. Of the 10,249 individuals who responded to vaccination questions from version 2 of the survey (summer 2021), 9770 (95.3%) had received the vaccine. After integration with EHR data up to the time of the survey completion, 1006 (4%) of the survey respondents had a discordant COVID-19 case status between EHRs and the survey. Using all longitudinal EHR and survey data, we identified 11,472 (6.4%) COVID-19-positive cases among Biobank participants. In comparison to COVID-19 cases identified through the survey, EHR-identified cases were younger and more likely to be Hispanic. CONCLUSIONS We found that the COVID-19 pandemic has had far-reaching and varying effects among our Biobank participants. Integrated data assets, such as the Biobank at the CCPM, are key resources for population health monitoring in response to public health emergencies, such as the COVID-19 pandemic.

Genetically regulated gene expression has helped elucidate the biological mechanisms underlying complex traits. Improved high-throughput technology allows similar interrogation of the genetically regulated proteome for understanding complex trait mechanisms. Here, we used the Trans-omics for Precision Medicine (TOPMed) Multi-omics pilot study, which comprises data from Multi-Ethnic Study of Atherosclerosis (MESA), to optimize genetic predictors of the plasma proteome for genetically regulated proteome-wide association studies (PWAS) in diverse populations. We built predictive models for protein abundances using data collected in TOPMed MESA, for which we have measured 1,305 proteins by a SOMAscan assay. We compared predictive models built via elastic net regression to models integrating posterior inclusion probabilities estimated by fine-mapping SNPs prior to elastic net. In order to investigate the transferability of predictive models across ancestries, we built protein prediction models in all four of the TOPMed MESA populations, African American (n=183), Chinese (n=71), European (n=416), and Hispanic/Latino (n=301), as well as in all populations combined. As expected, fine-mapping produced more significant protein prediction models, especially in African ancestries populations, potentially increasing opportunity for discovery. When we tested our TOPMed MESA models in the independent European INTERVAL study, fine-mapping improved cross-ancestries prediction for some proteins. Using GWAS summary statistics from the Population Architecture using Genomics and Epidemiology (PAGE) study, which comprises ~50,000 Hispanic/Latinos, African Americans, Asians, Native Hawaiians, and Native Americans, we applied S-PrediXcan to perform PWAS for 28 complex traits. The most protein-trait associations were discovered, colocalized, and replicated in large independent GWAS using proteome prediction model training populations with similar ancestries to PAGE. At current training population sample sizes, performance between baseline and fine-mapped protein prediction models in PWAS was similar, highlighting the utility of elastic net. Our predictive models in diverse populations are publicly available for use in proteome mapping methods athttps://doi.org/10.5281/zenodo.4837328.Author summaryGene regulation is a critical mechanism underlying complex traits. Transcriptome-wide association studies (TWAS) have helped elucidate potential mechanisms because each association connects a gene rather than a variant to the complex trait. Like genome-wide association studies (GWAS), most TWAS are still conducted exclusively in populations of European ancestry, which misses the opportunity to test the full spectrum of human genetic variation for associations with complex traits. Here, move beyond the transcriptome and because protein measurement assays are growing to allow interrogation of the proteome, we use data from TOPMed MESA to develop genetic predictors of protein abundance in diverse ancestry populations. We compare model-building strategies with the goal of providing the best resource for protein association discovery with available data. We demonstrate how these prediction models can be used to perform proteome-wide association studies (PWAS) in diverse populations. We show the most protein-trait associations were discovered, colocalized, and replicated in independent cohorts using proteome prediction model training populations with similar ancestries to individuals in the GWAS. We shared our protein prediction models and performance statistics publicly to facilitate future proteome mapping studies in diverse populations.

SummaryBiobanks are being established across the world to understand the genetic, environmental, and epidemiological basis of human diseases with the goal of better prevention and treatments. Genome-wide association studies (GWAS) have been very successful at mapping genomic loci for a wide range of human diseases and traits, but in general, lack appropriate representation of diverse ancestries - with most biobanks and preceding GWAS studies composed of individuals of European ancestries. Here, we introduce the Global Biobank Meta-analysis Initiative (GBMI) -- a collaborative network of 19 biobanks from 4 continents representing more than 2.1 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWAS generated using harmonized genotypes and phenotypes from member biobanks. GBMI brings together results from GWAS analysis across 6 main ancestry groups: approximately 33,000 of African ancestry either from Africa or from admixed-ancestry diaspora (AFR), 18,000 admixed American (AMR), 31,000 Central and South Asian (CSA), 341,000 East Asian (EAS), 1.4 million European (EUR), and 1,600 Middle Eastern (MID) individuals. In this flagship project, we generated GWASs from across 14 exemplar diseases and endpoints, including both common and less prevalent diseases that were previously understudied. Using the genetic association results, we validate that GWASs conducted in biobanks worldwide can be successfully integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics between biobanks. We demonstrate the value of this collaborative effort to improve GWAS power for diseases, increase representation, benefit understudied diseases, and improve risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of the studied traits.

Throughout human evolutionary history, large-scale migrations have led to intermixing (i.e., admixture) between previously separated human groups. Although classical and recent work have shown that studying admixture can yield novel historical insights, the extent to which this process contributed to adaptation remains underexplored. Here, we introduce a novel statistical model, specific to admixed populations, that identifies loci under selection while determining whether the selection likely occurred post-admixture or prior to admixture in one of the ancestral source populations. Through extensive simulations, we show that this method is able to detect selection, even in recently formed admixed populations, and to accurately differentiate between selection occurring in the ancestral or admixed population. We apply this method to genome-wide SNP data of ∼4,000 individuals in five admixed Latin American cohorts from Brazil, Chile, Colombia, Mexico, and Peru. Our approach replicates previous reports of selection in the human leukocyte antigen region that are consistent with selection post-admixture. We also report novel signals of selection in genomic regions spanning 47 genes, reinforcing many of these signals with an alternative, commonly used local-ancestry-inference approach. These signals include several genes involved in immunity, which may reflect responses to endemic pathogens of the Americas and to the challenge of infectious disease brought by European contact. In addition, some of the strongest signals inferred to be under selection in the Native American ancestral groups of modern Latin Americans overlap with genes implicated in energy metabolism phenotypes, plausibly reflecting adaptations to novel dietary sources available in the Americas.

Background Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR1. Host epigenome manipulation post coronavirus infection2–4 suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation. Methods We customized Illumina’s Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. Results Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. Conclusions In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections., Plain language summary Viral infections affect the body in many ways, including via changes to the epigenome, the sum of chemical modifications to an individual’s collection of genes that affect gene activity. Here, we analyzed the epigenome in blood samples from people with and without COVID-19 to determine whether we could find changes consistent with SARS-CoV-2 infection. Using a combination of statistical and machine learning techniques, we identify markers of SARS-CoV-2 infection as well as of severity and progression of COVID-19 disease. These signals of disease progression were present from the initial blood draw when first walking into the hospital. Together, these approaches demonstrate the potential of measuring the epigenome for monitoring SARS-CoV-2 status and severity., Konigsberg et al. profile DNA methylation in blood samples from SARS-CoV-2 cases and controls. The authors use machine learning to classify infected vs. non-infected individuals and predict clinical outcomes related to disease severity.

Funding Information: The work of the contributing biobanks was supported by numerous grants from governmental and charitable bodies. Biobank-specific acknowledgments and more detailed acknowledgments are included in Data S2. Initiative management, S.B.C. J.C. N.J.C. M.J.D. E.E.K. A.R.M. B.M.N. Y.O. A.V.P. D.A.v.H. R.G.W. C.J.W. W.Z. and S.Z.; individual biobank analysis, A.B. Y.B. B.M.B. C.D.B. S.C. T.-T.C. K.C. S.M.D. M.D. G.H.d.B. Y.D. N.J.D. M.-J.F. Y.-C.A.F. S.F. V.L.F. L.G.F. E.R.G. T.R.G. D.H.G. C.R.G. G.G.-A. S.E.G. L.A.G. C.H. J.B.H. W.E.H. H.H. K.H. N.I. A.I. R.J. M. Kurki, J.K. N.K. E.E.K. J.T.K. M. Kanai, T.L. K.L. M.H.L. S.L. K.L. Y.-F.L. V.L.F. R.J.F.L. E.A.L.-M. A.R.-M. S.M.-G. R.M. R.E.M. H.C.M. A.R.M. Y.M. H.M. S.E.M. I.Y.M. B.M. S.M. K.N. S.N. M.A.N.-A. K.N. Y.O. P.P. A.L.-P. A.P. B.P. S.P. M.H.P. D.J.R. N.R. M.D.R. A.R. C.S. S.S. S.S.S. J.A.S. P.S. I.S. T.T. R.T. K.T. J.U. D.A.v.H. B.V. M.V. Y.V. J.M.V. R.G.W. Y.W. S.J.W. B.N.W. K.-H.H.W. M.Z. X.Z. and S.Z.; individual biobank management, N.A. A.A.T. K.M.A.-D. P.A. K.C.B. M. Boehnke, M. Boezen, C.D.B. A.C. Z.C. C.-Y.C. J.C. N.J.C. S.M.D. S.F. Y.-C.A.F. S.F. E.F. T.G. C.R.G. C.J.G. Y.G. H.H. K.A.H. K.H. S.I.I. N.M.J. N.K. E.E.K. J.T.K. C.L. M.H.L. M.T.M.L. L.L. K.L. Y.-F.L. R.J.F.L. J.L. S.M. Y.M. K.M. I.Y.M. Y.O. C.M.O. A.V.P. B.P. D.J.P. D.J.R. M.D.R. S.S. J.W.S. H.S. K.S. T.T. U.T. R.C.T. D.A.v.H. M.V. R.G.W. D.C.W. C.W. J.W. M.Z. X.Z. and S.Z.; study design and interpretation of results, A.B. M. Boehnke, M. Boezen, B.M.B. T.-T.C. C.-Y.C. M.J.D. G.D.S. N.J.D. S.F. M.-J.F. H.K.F. E.R.G. A.G. T.G. J.B.H. J.H. K.H. R.J. M.K. E.E.K. T.K. C.M.L. V.L.F. E.A.L.-M. A.R.M. S.N. B.M.N. C.M.O. J.J.P. B.P. N.R. H.R. J.A.S. I.S. K.T. D.A.v.H. R.G.W. Y.W. D.C.W. S.J.W. C.J.W. B.N.W. J.W. K.-H.H.W. M.Z. H.Z. J.Z. W.Z. X.Z. and S.Z.; drafted and edited the paper, A.B. M. Boehnke, M. Boezen, M.J.D. G.H.d.B. N.J.D. T.R.G. J.B.H. N.I. N.M.J. M.K. V.L.F. S.M. A.R.M. H.M. S.N. B.M.N. C.M.O. B.P. H.R. C.S. J.A.S. J.W.S. K.T. Y.W. D.C.W. C.J.W. K.-H.H.W. H.Z. J.Z. W.Z. and S.Z.; primary meta-analysis and quality control, M.J.D. H.K.F. M. Kanai, J.K. J.T.K. M. Kurki, M.M. B.M.N. C.J.W. K.-H.H.W. and W.Z.; drug discovery: S.N. T.K. K.-H.H.W. W.Z. and Y.O.; fine mapping, M. Kanai, W.Z. M.J.D. and H.K.F.; polygenic risk score, Y.W. S.N. E.A.L.-M. S.K. K.T. K.L. M. Kanai, W.Z. K.W. M.-J.F. L.B. P.A. P.D. V.L.F. R.M. Y.M. B.B. S.S. J.U. E.R.G. N.J.C. I.S. Y.O. A.R.M. and J.B.H.; proteome-wide Mendelian randomization, H.Z. H.R. A.B. G.H. G.D.S. B.M.B. W.Z. B.M.N. T.R.G. and J.Z.; transcriptome-wide association study, A.B. J.B.H. W.Z. J.Z. M. Kanai, B.P. E.R.G. and N.J.C.; asthma, K.T. W.Z. Y.W. M. Kanai, S.N. Y.O. B.M.N. M.J.D. and A.R.M.; heart failure, K.-H.H.W. N.J.D. B.N.W. I.S. S.E.G. J.B.H. N.J.C. M.P. R.J.F.L. M.J.D. B.M.N. W.Z. W.E.H. and C.J.W.; idiopathic pulmonary fibrosis, J.J.P. W.Z. M.J.D. J.T.K. N.J.C. and J.B.H.; primary open-angle glaucoma, V.L.F. A.B. W.Z. Y.W. K.L. M. Kanai, E.A.L.-M. P.S. R.T. X.Z. S.N. S.S. Y.O. N.I. S.M. H.S. I.S. C.W. A.R.M. E.R.G. N.M.J. N.J.C. and J.B.H.; stroke, I.S. K.-H.H.W. W.H. B.N.W. W.Z. J.E.H. A.P. B.B. A.H.S. M.E.G. R.G.W. K.H. C.K. S.Z. M.J.D. B.M.N. and C.J.W.; venous thromboembolism, B.N.W. I.S. K.-H.H.W. B.B. V.L.F. K.T. M.D. B.N. W.Z. J.A.S. and C.J.W. All authors reviewed the manuscript. M.J.D. is a founder of Maze Therapeutics. B.M.N. is a member of the scientific advisory board at Deep Genomics and a consultant for Camp4 Therapeutics, Takeda Pharmaceutical, and Biogen. The spouse of C.J.W. works at Regeneron Pharmaceuticals. C.-Y.C. is employed by Biogen. C.R.G. owns stock in 23andMe, Inc. T.R.G. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. E.E.K. has received speaker fees from Regeneron, Illumina, and 23andMe and is a member of the advisory board for Galateo Bio. R.E.M. has received speaker fees from Illumina and is a scientific advisor to the Epigenetic Clock Development Foundation. G.D.S. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. K.S. and U.T. are employed by deCODE Genetics/Amgen, Inc. J.Z. has received research funding from various pharmaceutical companies to support the application of Mendelian randomization to drug target prioritization. S.M. is a co-founder of and holds stock in Seonix Bio. Publisher Copyright: © 2022 Biobanks facilitate genome-wide association studies (GWASs), which have mapped genomic loci across a range of human diseases and traits. However, most biobanks are primarily composed of individuals of European ancestry. We introduce the Global Biobank Meta-analysis Initiative (GBMI)—a collaborative network of 23 biobanks from 4 continents representing more than 2.2 million consented individuals with genetic data linked to electronic health records. GBMI meta-analyzes summary statistics from GWASs generated using harmonized genotypes and phenotypes from member biobanks for 14 exemplar diseases and endpoints. This strategy validates that GWASs conducted in diverse biobanks can be integrated despite heterogeneity in case definitions, recruitment strategies, and baseline characteristics. This collaborative effort improves GWAS power for diseases, benefits understudied diseases, and improves risk prediction while also enabling the nomination of disease genes and drug candidates by incorporating gene and protein expression data and providing insight into the underlying biology of human diseases and traits.

Since the initial reported discovery of SARS-CoV-2 in late 2019, genomic surveillance has been an important tool to understand its transmission and evolution. Here, we describe a case study of genomic sequencing of Colorado SARS-CoV-2 samples collected August through November 2020 at the University of Colorado Anschutz Medical campus in Aurora and the United States Air Force Academy in Colorado Springs. We obtained nearly complete sequences for 44 genomes, inferred ancestral sequences shared among these local samples, and used NextStrain variant and clade frequency monitoring in North America to place the Colorado sequences into their continental context. Furthermore, we describe genomic monitoring of a lineage that likely originated in the local Colorado Springs community and expanded rapidly over the course of two months in an outbreak within the well-controlled environment of the United States Air Force Academy. This variant contained a number of amino acid-altering mutations that may have contributed to its spread, but it appears to have been controlled using extensive contact tracing and strict quarantine protocols. The genome sequencing allowed validation of the transmission pathways inferred by the United States Air Force Academy and provides a window into the evolutionary process and transmission dynamics of a potentially dangerous but ultimately contained variant.SIGNIFICANCESARS-CoV-2 spreads and mutates, negatively impacting containment. In this study, we use long-read sequencing to generate 44 SARS-CoV-2 genomes from COVID-19 patients associated with a rapid-spreading event on the USAFA campus, as well as a neighboring community for reference. We reconstruct the genomic and evolutionary signatures of the rapid-spreading event, and pin-point novel, protein-altering mutations that may have impacted viral fitness. These insights into viral evolutionary dynamics, in the context of contact tracing and a rigorous containment program, help to inform response efforts in the future.

Identity-by-descent (IBD), the detection of shared segments inherited from a common ancestor, is a fundamental concept in genomics with broad applications in the characterization and analysis of genomes. While historically the concept of IBD was extensively utilized through linkage analyses and in studies of founder populations, applications of IBD-based methods subsided during the genome-wide association study era. This was primarily due to the computational expense of IBD detection, which becomes increasingly relevant as the field moves toward the analysis of biobank-scale datasets that encompass individuals from highly diverse backgrounds. To address these computational barriers, the past several years have seen new methodological advances enabling IBD detection for datasets in the hundreds of thousands to millions of individuals, enabling novel analyses at an unprecedented scale. Here, we describe the latest innovations in IBD detection and describe opportunities for the application of IBD-based methods across a broad range of questions in the field of genomics.

Polynesia was settled in a series of extraordinary voyages across an ocean spanning one third of the Earth1, but the sequences of islands settled remain unknown and their timings disputed. Currently, several centuries separate the dates suggested by different archaeological surveys2–4. Here, using genome-wide data from merely 430 modern individuals from 21 key Pacific island populations and novel ancestry-specific computational analyses, we unravel the detailed genetic history of this vast, dispersed island network. Our reconstruction of the branching Polynesian migration sequence reveals a serial founder expansion, characterized by directional loss of variants, that originated in Samoa and spread first through the Cook Islands (Rarotonga), then to the Society (Tōtaiete mā) Islands (11th century), the western Austral (Tuha’a Pae) Islands and Tuāmotu Archipelago (12th century), and finally to the widely separated, but genetically connected, megalithic statue-building cultures of the Marquesas (Te Henua ‘Enana) Islands in the north, Raivavae in the south, and Easter Island (Rapa Nui), the easternmost of the Polynesian islands, settled in approximately ad 1200 via Mangareva. Analysis of genomic networks from 430 modern individuals across 21 Pacific island populations reveals the human settlement history of Polynesia.

We analyzed whole genome and RNA sequencing data from 2,733 African American and Hispanic/Latino children to explore ancestry- and heterozygosity-related differences in the genetic architecture of whole blood gene expression. We found that heritability of gene expression significantly increases with greater proportion of African genetic ancestry and decreases with higher levels of Indigenous American ancestry, consistent with a relationship between heterozygosity and genetic variance. Among heritable protein-coding genes, the prevalence of statistically significant ancestry-specific expression quantitative trait loci (anc-eQTLs) was 30% in African ancestry and 8% for Indigenous American ancestry segments. Most of the anc-eQTLs (89%) were driven by population differences in allele frequency, demonstrating the importance of measuring gene expression across multiple populations. Transcriptome-wide association analyses of multi-ancestry summary statistics for 28 traits identified 79% more gene-trait pairs using models trained in our admixed population than models trained in GTEx. Our study highlights the importance of large and ancestrally diverse genomic studies for enabling new discoveries of complex trait architecture and reducing disparities.

BackgroundGroups of distantly related individuals who share a short segment of their genome identical-by-descent (IBD) can provide insights about rare traits and diseases in massive biobanks via a process called IBD mapping. Clustering algorithms play an important role in finding these groups. We set out to analyze the fitness of commonly used, fast and scalable clustering algorithms for IBD mapping applications. We designed a realistic benchmark for local IBD graphs and utilized it to compare clustering algorithms in terms of statistical power. We also investigated the effectiveness of common clustering metrics as replacements for statistical power.ResultsWe simulated 3.4 million clusters across 850 experiments with varying cluster counts, false-positive, and false-negative rates. Infomap and Markov Clustering (MCL) community detection methods have high statistical power in most of the graphs, compared to greedy methods such as Louvain and Leiden. We demonstrate that standard clustering metrics, such as modularity, cannot predict statistical power of algorithms in IBD mapping applications, though they can help with simulating realistic benchmarks. We extend our findings to real datasets by analyzing 3 populations in the Population Architecture using Genomics and Epidemiology (PAGE) Study with 51,000 members and 2 million shared segments on Chromosome 1, resulting in the extraction of 39 million local IBD clusters across three different populations in PAGE. We used cluster properties derived in PAGE to increase the accuracy of our simulations and comparison.ConclusionsMarkov Clustering produces a 30% increase in statistical power compared to the current state-of-art approach, while reducing runtime by 3 orders of magnitude; making it computationally tractable in modern large-scale genetic datasets. We provide an efficient implementation to enable clustering at scale for IBD mapping and poplation-based linkage for various populations and scenarios.

The ability to identify segments of genomes identical-by-descent (IBD) is a part of standard workflows in both statistical and population genetics. However, traditional methods for finding local IBD across all pairs of individuals scale poorly leading to a lack of adoption in very large-scale datasets. Here, we present iLASH, an algorithm based on similarity detection techniques that shows equal or improved accuracy in simulations compared to current leading methods and speeds up analysis by several orders of magnitude on genomic datasets, making IBD estimation tractable for millions of individuals. We apply iLASH to the PAGE dataset of ~52,000 multi-ethnic participants, including several founder populations with elevated IBD sharing, identifying IBD segments in ~3 minutes per chromosome compared to over 6 days for a state-of-the-art algorithm. iLASH enables efficient analysis of very large-scale datasets, as we demonstrate by computing IBD across the UK Biobank (~500,000 individuals), detecting 12.9 billion pairwise connections., Traditional methods to identify genomic regions identical-by-descent (IBD) do not scale well to biobank-level datasets. Here, the authors describe a new IBD algorithm, iLASH, which uses LocAlity-Sensitive Hashing to provide rapid IBD estimation when applied to the PAGE and UK Biobank datasets.