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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.

Genome-wide association studies (GWAS) have identified hundreds of genetic risk loci for coronary artery disease (CAD). However, non-European populations are underrepresented in GWAS and the causal gene-regulatory mechanisms of these risk loci during atherosclerosis remain unclear. We incorporated local ancestry and haplotype information to identify quantitative trait loci (QTL) for gene expression and splicing in coronary arteries obtained from 138 ancestrally diverse Americans. Of 2,132 eQTL-associated genes (eGenes), 47% were previously unreported in coronary arteries and 19% exhibited cell-type-specific expression. Colocalization analysis with GWAS identified subgroups of eGenes unique to CAD and blood pressure. Fine-mapping highlighted additional eGenes of interest, includingTBX20andIL5. Splicing (s)QTLs for 1,690 genes were also identified, among whichTOR1AIP1andULK3sQTLs demonstrated the importance of evaluating splicing events to accurately identify disease-relevant gene expression. Our work provides the first human coronary artery eQTL resource from a patient sample and exemplifies the necessity of diverse study populations and multi-omic approaches to characterize gene regulation in critical disease processes.Study Design Overview

Coronary artery disease (CAD) and atherosclerosis are characterized by plaque formation in the arteries wall. CAD progression involves complex interactions and phenotypic plasticity within and between distinct vascular and immune cell lineages. Single-cell RNA-seq (scRNA-seq) studies have highlighted lineage-specific transcriptomic signatures, but the reported cell phenotypes in humans remain controversial. Here, we meta-analyzed four scRNA-seq datasets, creating the first map of human cell diversity in atherosclerosis. We generated an atlas of 118,578 high-quality cells, characterized cell-type diversity and provided insights into smooth muscle cell (SMC) phenotypic modulation, transcription factor activity and cell-cell communication. We integrated genome-wide association study (GWAS) data and uncovered a critical role for modulated SMC phenotypes in CAD and coronary calcification. Finally, we identified candidate markers of fibromyocyte and fibrochondrogenic human SMCs (LTBP1andCRTAC1) that may serve as proxies of atherosclerosis progression. Altogether, we created a unified cellular map of atherosclerosis informing cell state-specific mechanistic and translational studies of cardiovascular diseases.

Background Circulating white blood cell and platelet traits are clinically linked to various disease outcomes and differ across individuals and ancestry groups. Genetic factors play an important role in determining these traits and many loci have been identified. However, most of these findings were identified in populations of European ancestry (EA), with African Americans (AA), Hispanics/Latinos (HL), and other races/ethnicities being severely underrepresented. Results We performed ancestry-combined and ancestry-specific genome-wide association studies (GWAS) for white blood cell and platelet traits in the ancestrally diverse Population Architecture using Genomics and Epidemiology (PAGE) Study, including 16,201 AA, 21,347 HL, and 27,236 EA participants. We identified six novel findings at suggestive significance (P < 5E-8), which need confirmation, and independent signals at six previously established regions at genome-wide significance (P < 2E-9). We confirmed multiple previously reported genome-wide significant variants in the single variant association analysis and multiple genes using PrediXcan. Evaluation of loci reported from a Euro-centric GWAS indicated attenuation of effect estimates in AA and HL compared to EA populations. Conclusions Our results highlighted the potential to identify ancestry-specific and ancestry-agnostic variants in participants with diverse backgrounds and advocate for continued efforts in improving inclusion of racially/ethnically diverse populations in genetic association studies for complex traits.

Background: Atherosclerotic plaque progression to coronary artery disease is the leading cause of death worldwide. The basic pathophysiology of atherosclerosis is well described, but underlying genetic mechanisms of contributing cell types remain incompletely characterized. We utilized human coronary artery RNA sequencing data to identify differentially expressed genes and genetic variants that affect expression. Methods: We used bulk RNA sequencing data from coronary artery samples from 138 American adults (30% female) representing a broad phenotypic range of atherosclerosis. To account for ancestral diversity, we incorporated local ancestry into permutation-based eQTL analyses using a combination of published methods. We also used mixQTL to evaluate the contribution of allele-specific expression to identifying eQTLs. All analyses adjusted for age, sex, and ancestry. We utilized CAD GWAS summary statistics, coronary artery ENCODE annotations, and activity-by-contact (ABC) scores to fine-map eGenes. Results: In local-ancestry eQTL analyses, we report 467 eGenes (FDR Conclusion: Understanding changes in the gene expression program at all disease stages will help characterize coronary artery disease processes as well as identify potential therapeutic targets for functional examination. This work represents a step toward characterizing CAD-related gene expression programs and highlight the importance of inclusive, deliberate study design.

Background Quantitative red blood cell (RBC) traits are highly polygenic clinically relevant traits, with approximately 500 reported GWAS loci. The majority of RBC trait GWAS have been performed in European- or East Asian-ancestry populations, despite evidence that rare or ancestry-specific variation contributes substantially to RBC trait heritability. Recently developed combined-phenotype methods which leverage genetic trait correlation to improve statistical power have not yet been applied to these traits. Here we leveraged correlation of seven quantitative RBC traits in performing a combined-phenotype analysis in a multi-ethnic study population. Results We used the adaptive sum of powered scores (aSPU) test to assess combined-phenotype associations between ~ 21 million SNPs and seven RBC traits in a multi-ethnic population (maximum n = 67,885 participants; 24% African American, 30% Hispanic/Latino, and 43% European American; 76% female). Thirty-nine loci in our multi-ethnic population contained at least one significant association signal (p 5%) across all ancestral populations. Nineteen additional independent association signals were identified at seven known loci (HFE, KIT, HBS1L/MYB, CITED2/FILNC1, ABO, HBA1/2, and PLIN4/5). For example, the HBA1/2 locus contained 14 conditionally independent association signals, 11 of which were previously unreported and are specific to African and Amerindian ancestries. One variant in this region was common in all ancestries, but exhibited a narrower LD block in African Americans than European Americans or Hispanics/Latinos. GTEx eQTL analysis of all independent lead SNPs yielded 31 significant associations in relevant tissues, over half of which were not at the gene immediately proximal to the lead SNP. Conclusion This work identified seven loci containing multiple independent association signals for RBC traits using a combined-phenotype approach, which may improve discovery in genetically correlated traits. Highly complex genetic architecture at the HBA1/2 locus was only revealed by the inclusion of African Americans and Hispanics/Latinos, underscoring the continued importance of expanding large GWAS to include ancestrally diverse populations.

Over the last few years, new high-throughput biotechnologies and bioinformatic methods are revolutionizing our way of deep profiling tissue specimens at the molecular levels. These recent innovations provide opportunities to advance our understanding of atherosclerosis using human lesions aborted during autopsies and cardiac surgeries. Studies on human lesions have been focusing on understanding the relationship between molecules in the lesions with tissue morphology, genetic risk of atherosclerosis, and future adverse cardiovascular events. This review will highlight ways to utilize human atherosclerotic lesions in translational research by work from large cardiovascular biobanks to tissue registries. We will also discuss the opportunities and challenges of working with human atherosclerotic lesions in the era of next-generation sequencing.

Histopathological studies have revealed key processes of atherosclerotic plaque thrombosis. However, the diversity and complexity of lesion types highlight the need for improved sub- phenotyping. We hypothesized that unbiased clustering of plaques based on gene expression results in an alternative categorization of late-stage atherosclerotic lesions.We analyzed the gene expression profiles of 654 advanced human carotid plaques. The unsupervised, transcriptome-driven clustering revealed five dominant plaque types. These novel plaque phenotypes associated with clinical presentation (pIn conclusion, the definition of the plaque at risk for a thrombotic event can be fine-tuned by in- depth transcriptomic based phenotyping. These differential plaque phenotypes prove clinically relevant for both carotid and coronary artery plaques and point to differential underlying biology of symptomatic lesions.

Background and aims The atherosclerotic plaque microenvironment is highly complex, and selective agents that modulate plaque stability are not yet available. We sought to develop a scRNA-seq analysis workflow to investigate this environment and uncover potential therapeutic approaches. We designed a user-friendly, reproducible workflow that will be applicable to other disease-specific scRNA-seq datasets. Methods Here we incorporated automated cell labeling, pseudotemporal ordering, ligand-receptor evaluation, and drug-gene interaction analysis into a ready-to-deploy workflow. We applied this pipeline to further investigate a previously published human coronary single-cell dataset by Wirka et al. Notably, we developed an interactive web application to enable further exploration and analysis of this and other cardiovascular single-cell datasets. Results We revealed distinct derivations of fibroblast-like cells from smooth muscle cells (SMCs), and showed the key changes in gene expression along their de-differentiation path. We highlighted several key ligand-receptor interactions within the atherosclerotic environment through functional expression profiling and revealed several avenues for future pharmacological development for precision medicine. Further, our interactive web application, PlaqView ( www.plaqview.com ), allows lay scientists to explore this and other datasets and compare scRNA-seq tools without prior coding knowledge. Conclusions This publicly available workflow and application will allow for more systematic and user-friendly analysis of scRNA datasets in other disease and developmental systems. Our analysis pipeline provides many hypothesis-generating tools to unravel the etiology of coronary artery disease. We also highlight potential mechanisms for several drugs in the atherosclerotic cellular environment. Future releases of PlaqView will feature more scRNA-seq and scATAC-seq atherosclerosis-related datasets to provide a critical resource for the field, and to promote data harmonization and biological interpretation.

Coronary artery disease (CAD) is a complex inflammatory disease involving genetic influences across several cell types. Genome-wide association studies (GWAS) have identified over 170 loci associated with CAD, where the majority of risk variants reside in noncoding DNA sequences impacting cis -regulatory elements (CREs). Here, we applied single-cell ATAC-seq to profile 28,316 cells across coronary artery segments from 41 patients with varying stages of CAD, which revealed 14 distinct cellular clusters. We mapped over 320,000 accessible sites across all cells, identified cell type-specific elements, transcription factors, and prioritized functional CAD risk variants via quantitative trait locus and sequence-based predictive modeling. Using differential peak analyses we identified a number of candidate mechanisms for smooth muscle cell transition states (e.g. fibromyocytes). By integrating these profiles with GWAS meta-analysis summary data we resolved cell type-specific putative binding sites for the majority of CAD risk variants. In particular, we prioritized functional variants predicted to alter MEF2 binding in smooth muscle cells at the MRAS locus. We also identify variants predicted to alter macrophage-specific regulation of LIPA. We further employed DNA to gene linkage to nominate disease-associated key driver transcription factors such as PRDM16 and TBX2. Together, this single cell atlas provides a critical step towards interpreting cis -regulatory mechanisms in the vessel wall across the continuum of CAD risk.

Coronary artery disease (CAD) is a complex inflammatory disease involving genetic influences across several cell types. Genome-wide association studies (GWAS) have identified over 170 loci associated with CAD, where the majority of risk variants reside in noncoding DNA sequences impacting cis-regulatory elements (CREs). Here, we applied single-cell ATAC-seq to profile 28,316 cells across coronary artery segments from 41 patients with varying stages of CAD, which revealed 14 distinct cellular clusters. We mapped ~320,000 accessible sites across all cells, identified cell type-specific elements, transcription factors, and prioritized functional CAD risk variants via quantitative trait locus and sequence-based predictive modeling. We identified a number of candidate mechanisms for smooth muscle cell transition states and identified putative binding sites for risk variants. We further employed CRE to gene linkage to nominate disease-associated key driver transcription factors such as PRDM16 and TBX2. This single cell atlas provides a critical step towards interpreting cis-regulatory mechanisms in the vessel wall across the continuum of CAD risk.

Background: Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Also, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed. Methods: To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including N=229 African American and N=381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, N = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (N = 27,955) and Hispanic/Latino (N = 28,324) ancestry participants. Results: Our results revealed 24 suggestive signals (p < 1×10^(-4)) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (N=802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN. Conclusions: These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.

Coronary artery disease (CAD) is a complex inflammatory disease involving genetic influences across cell types. Genome-wide association studies have identified over 200 loci associated with CAD, where the majority of risk variants reside in noncoding DNA sequences impacting cis-regulatory elements. Here, we applied single-nucleus assay for transposase-accessible chromatin with sequencing to profile 28,316 nuclei across coronary artery segments from 41 patients with varying stages of CAD, which revealed 14 distinct cellular clusters. We mapped ~320,000 accessible sites across all cells, identified cell-type-specific elements and transcription factors, and prioritized functional CAD risk variants. We identified elements in smooth muscle cell transition states (for example, fibromyocytes) and functional variants predicted to alter smooth muscle cell- and macrophage-specific regulation of MRAS (3q22) and LIPA (10q23), respectively. We further nominated key driver transcription factors such as PRDM16 and TBX2. Together, this single-nucleus atlas provides a critical step towards interpreting regulatory mechanisms across the continuum of CAD risk.

Genome-wide association studies have been successful mapping loci for individual phenotypes, but few studies have comprehensively interrogated evidence of shared genetic effects across multiple phenotypes simultaneously. Statistical methods have been proposed for analyzing multiple phenotypes using summary statistics, which enables studies of shared genetic effects while avoiding challenges associated with individual-level data sharing. Adaptive tests have been developed to maintain power against multiple alternative hypotheses because the most powerful single-alternative test depends on the underlying structure of the associations between the multiple phenotypes and a single nucleotide polymorphism (SNP). Here we compare the performance of six such adaptive tests: two adaptive sum of powered scores (aSPU) tests, the unified score association test (metaUSAT), the adaptive test in a mixed-models framework (mixAda) and two principal-component-based adaptive tests (PCAQ and PCO). Our simulations highlight practical challenges that arise when multivariate distributions of phenotypes do not satisfy assumptions of multivariate normality. Previous reports in this context focus on low minor allele count (MAC) and omit the aSPU test, which relies less than other methods on asymptotic and distributional assumptions. When these assumptions are not satisfied, particularly when MAC is low and/or phenotype covariance matrices are singular or nearly singular, aSPU better preserves type I error, sometimes at the cost of decreased power. We illustrate this trade-off with multiple phenotype analyses of six quantitative electrocardiogram traits in the Population Architecture using Genomics and Epidemiology (PAGE) study.

Background and AimsThe atherosclerotic plaque microenvironment is highly complex, and selective agents that modulate plaque stability or other plaque phenotypes are not yet available. We sought to investigate the human atherosclerotic cellular environment using scRNA-seq to uncover potential therapeutic approaches. We aimed to make our workflow user-friendly, reproducible, and applicable to other disease-specific scRNA-seq datasets.MethodsHere we incorporate automated cell labeling, pseudotemporal ordering, ligand-receptor evaluation, and drug-gene interaction analysis into an enhanced and reproducible scRNA-seq analysis workflow. Notably, we also developed an R Shiny based interactive web application to enable further exploration and analysis of the scRNA dataset.ResultsWe applied this analysis workflow to a human coronary artery scRNA dataset and revealed distinct derivations of chondrocyte-like and fibroblast-like cells from smooth muscle cells (SMCs), and show the key changes in gene expression along their de-differentiation path. We highlighted several key ligand-receptor interactions within the atherosclerotic environment through functional expression profiling and revealed several attractive avenues for future pharmacological repurposing in precision medicine. Further, our interactive web application, PlaqView (www.plaqview.com), allows other researchers to easily explore this dataset and benchmark applicable scRNA-seq analysis tools without prior coding knowledge.ConclusionsThese results suggest novel effects of chemotherapeutics on the atherosclerotic cellular environment and provide future avenues of studies in precision medicine. This publicly available workflow will also allow for more systematic and user-friendly analysis of scRNA datasets in other disease and developmental systems. PlaqView allows for rapid visualization and analysis of atherosclerosis scRNA-seq datasets without the need of prior coding experience. Future releases of PlaqView will feature additional larger scRNA-seq and scATAC-seq atherosclerosis-related datasets, thus providing a critical resource for the field by promoting data harmonization and biological interpretation.

Background: We examined how expanding electrocardiographic trait genome-wide association studies to include ancestrally diverse populations, prioritize more precise phenotypic measures, and evaluate evidence for shared genetic effects enabled the detection and characterization of loci. Methods: We decomposed 10 seconds, 12-lead electrocardiograms from 34 668 multi-ethnic participants (15% Black; 30% Hispanic/Latino) into 6 contiguous, physiologically distinct (P wave, PR segment, QRS interval, ST segment, T wave, and TP segment) and 2 composite, conventional (PR interval and QT interval) interval scale traits and conducted multivariable-adjusted, trait-specific univariate genome-wide association studies using 1000-G imputed single-nucleotide polymorphisms. Evidence of shared genetic effects was evaluated by aggregating meta-analyzed univariate results across the 6 continuous electrocardiographic traits using the combined phenotype adaptive sum of powered scores test. Results: We identified 6 novels ( CD36, PITX2, EMB, ZNF592, YPEL2 , and BC043580 ) and 87 known loci (adaptive sum of powered score test P −9 ). Lead single-nucleotide polymorphism rs3211938 at CD36 was common in Blacks (minor allele frequency=10%), near monomorphic in European Americans, and had effects on the QT interval and TP segment that ranked among the largest reported to date for common variants. The other 5 novel loci were observed when evaluating the contiguous but not the composite electrocardiographic traits. Combined phenotype testing did not identify novel electrocardiographic loci unapparent using traditional univariate approaches, although this approach did assist with the characterization of known loci. Conclusions: Despite including one-third as many participants as published electrocardiographic trait genome-wide association studies, our study identified 6 novel loci, emphasizing the importance of ancestral diversity and phenotype resolution in this era of ever-growing genome-wide association studies.

Background: Thousands of genetic variants have been associated with hematological traits, though target genes remain unknown at most loci. Moreover, limited analyses have been conducted in African ancestry and Hispanic/Latino populations; hematological trait associated variants more common in these populations have likely been missed. Methods: To derive gene expression prediction models, we used ancestry-stratified datasets from the Multi-Ethnic Study of Atherosclerosis (MESA, including n = 229 African American and n = 381 Hispanic/Latino participants, monocytes) and the Depression Genes and Networks study (DGN, n = 922 European ancestry participants, whole blood). We then performed a transcriptome-wide association study (TWAS) for platelet count, hemoglobin, hematocrit, and white blood cell count in African (n = 27,955) and Hispanic/Latino (n = 28,324) ancestry participants. Results: Our results revealed 24 suggestive signals (p < 1 × 10−4) that were conditionally distinct from known GWAS identified variants and successfully replicated these signals in European ancestry subjects from UK Biobank. We found modestly improved correlation of predicted and measured gene expression in an independent African American cohort (the Genetic Epidemiology Network of Arteriopathy (GENOA) study (n = 802), lymphoblastoid cell lines) using the larger DGN reference panel; however, some genes were well predicted using MESA but not DGN. Conclusions: These analyses demonstrate the importance of performing TWAS and other genetic analyses across diverse populations and of balancing sample size and ancestry background matching when selecting a TWAS reference panel.

The incidence of out-of-hospital sudden unexpected death (OHSUD) in a racially and socioeconomically diverse population has been inadequately studied. We collated all OHSUDs over a 24-month period among 18- to 64-year olds in Wake County, North Carolina, to investigate geographic and socioeconomic disparity in incidence of OHSUD. An electronic query of Wake County Emergency Medical Services (EMS) identified all EMS attended out-of-hospital deaths. After excluding trauma, expected deaths, and deaths occurring in non–free-living subjects, medical records and medical examiner's reports were reviewed by a committee of cardiologists to make the determination of OHSUD. Victims were geocoded to census tracts, and demographic and socioeconomic data were obtained from the 2014 American Community Survey and 2010 US Census. Incidence was examined by sociodemographic group with univariate analysis and multivariable regression. There were 397 OHSUDs, and 53% of census tracts had >1 event. The incidence of OHSUD was 64 of 100,000; 107 of 100,000 among blacks; and 60 of 100,000 among whites. Census tracts with >1 OHSUD had a higher population of blacks, a greater proportion unmarried, a lower median household income, and a greater proportion residing in a rural area. Only median household income remained a significant predictor of OHSUD after adjustment in multivariable analysis. Low median household income of a community portends a higher incidence of sudden death. In conclusion, interventions to reduce the incidence of sudden death need to be developed with these specific communities in mind.

Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11–34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count 86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations., Author summary Admixed African and Hispanic/Latino populations remain understudied in genetic studies of complex diseases. These populations have more complex linkage disequilibrium (LD) structure that can impair mapping of variants. Genotype imputation represents an approach to improve genome coverage, especially for rare or ancestry-specific variation; however, these understudied populations also have smaller relevant imputation reference panels. In this study, we leveraged >100,000 phased sequences generated from the multi-ethnic NHLBI TOPMed project for imputation in ~21,600 individuals of African ancestry (AAs) and ~21,700 Hispanics/Latinos. We demonstrated substantially higher imputation quality for low frequency and rare variants in comparison to the 1000 Genomes Project and Haplotype Reference Consortium reference panels. Analysis of quantitative hematological traits led to the discovery of associations with two rare variants in the HBB gene; one of these variants was replicated in an independent sample, and the other is known to cause anemia in the homozygous state. By comparison, the same HBB variants would not have been genome-wide significant using current reference panels due to lower imputation quality. Our findings demonstrate the power of TOPMed whole genome sequencing data for imputation and subsequent association analysis in admixed African and Hispanic/Latino populations.

The past decade has seen a technological revolution in human genetics that has empowered population-level investigations into genetic associations with phenotypes. Although these discoveries rely on genetic variation across individuals, association studies have overwhelmingly been performed in populations of European descent. In this review, we describe limitations faced by single-population studies and provide an overview of strategies to improve global representation in existing data sets and future human genomics research via diversity-focused, multiethnic studies. We highlight the successes of individual studies and meta-analysis consortia that have provided unique knowledge. Additionally, we outline the approach taken by the Population Architecture Using Genomics and Epidemiology (PAGE) study to develop best practices for performing genetic epidemiology in multiethnic contexts. Finally, we discuss how limiting investigations to single populations impairs findings in the clinical domain for both rare-variant identification and genetic risk prediction.

Red blood cell (RBC) traits provide insight into a wide range of physiological states and exhibit moderate to high heritability, making them excellent candidates for genetic studies to inform underlying biologic mechanisms. Previous RBC trait genome-wide association studies were performed primarily in European- or Asian-ancestry populations, missing opportunities to inform understanding of RBC genetic architecture in diverse populations and reduce intervals surrounding putative functional SNPs through fine-mapping. Here, we report the first fine-mapping of six correlated (Pearson's r range: |0.04 - 0.92|) RBC traits in up to 19,036 African Americans and 19,562 Hispanic/Latinos participants of the Population Architecture using Genomics and Epidemiology (PAGE) consortium. Trans-ethnic meta-analysis of race/ethnic- and study-specific estimates for approximately 11,000 SNPs flanking 13 previously identified association signals as well as 150,000 additional array-wide SNPs was performed using inverse-variance meta-analysis after adjusting for study and clinical covariates. Approximately half of previously reported index SNP-RBC trait associations generalized to the trans-ethnic study population (p

Summary/AbstractGenome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development, and clinical guidelines. However, the dominance of European-ancestry populations in GWAS creates a biased view of the role of human variation in disease, and hinders the equitable translation of genetic associations into clinical and public health applications. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioral phenotypes in 49,839 non-European individuals. Using strategies designed for analysis of multi-ethnic and admixed populations, we confirm 574 GWAS catalog variants across these traits, and find 38 secondary signals in known loci and 27 novel loci. Our data shows strong evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts, and insights into clinical implications. We strongly advocate for continued, large genome-wide efforts in diverse populations to reduce health disparities.

Genome-wide association studies (GWAS) have laid the foundation for investigations into the biology of complex traits, drug development and clinical guidelines. However, the majority of discovery efforts are based on data from populations of European ancestry1–3. In light of the differential genetic architecture that is known to exist between populations, bias in representation can exacerbate existing disease and healthcare disparities. Critical variants may be missed if they have a low frequency or are completely absent in European populations, especially as the field shifts its attention towards rare variants, which are more likely to be population-specific4–10. Additionally, effect sizes and their derived risk prediction scores derived in one population may not accurately extrapolate to other populations11,12. Here we demonstrate the value of diverse, multi-ethnic participants in large-scale genomic studies. The Population Architecture using Genomics and Epidemiology (PAGE) study conducted a GWAS of 26 clinical and behavioural phenotypes in 49,839 non-European individuals. Using strategies tailored for analysis of multi-ethnic and admixed populations, we describe a framework for analysing diverse populations, identify 27 novel loci and 38 secondary signals at known loci, as well as replicate 1,444 GWAS catalogue associations across these traits. Our data show evidence of effect-size heterogeneity across ancestries for published GWAS associations, substantial benefits for fine-mapping using diverse cohorts and insights into clinical implications. In the United States—where minority populations have a disproportionately higher burden of chronic conditions13—the lack of representation of diverse populations in genetic research will result in inequitable access to precision medicine for those with the highest burden of disease. We strongly advocate for continued, large genome-wide efforts in diverse populations to maximize genetic discovery and reduce health disparities. © 2019, The Author(s), under exclusive licence to Springer Nature Limited.

Prior GWAS have identified loci associated with red blood cell (RBC) traits in populations of European, African, and Asian ancestry. These studies have not included individuals with an Amerindian ancestral background, such as Hispanics/Latinos, nor evaluated the full spectrum of genomic variation beyond single nucleotide variants. Using a custom genotyping array enriched for Amerindian ancestral content and 1000 Genomes imputation, we performed GWAS in 12,502 participants of Hispanic Community Health Study and Study of Latinos (HCHS/SOL) for hematocrit, hemoglobin, RBC count, RBC distribution width (RDW), and RBC indices. Approximately 60% of previously reported RBC trait loci generalized to HCHS/SOL Hispanics/Latinos, including African ancestral alpha- and beta-globin gene variants. In addition to the known 3.8kb alpha-globin copy number variant, we identified an Amerindian ancestral association in an alpha-globin regulatory region on chromosome 16p13.3 for mean corpuscular volume and mean corpuscular hemoglobin. We also discovered and replicated three genome-wide significant variants in previously unreported loci for RDW (SLC12A2 rs17764730, PSMB5 rs941718), and hematocrit (PROX1 rs3754140). Among the proxy variants at the SLC12A2 locus we identified rs3812049, located in a bi-directional promoter between SLC12A2 (which encodes a red cell membrane ion-transport protein) and an upstream anti-sense long-noncoding RNA, LINC01184, as the likely causal variant. We further demonstrate that disruption of the regulatory element harboring rs3812049 affects transcription of SLC12A2 and LINC01184 in human erythroid progenitor cells. Together, these results reinforce the importance of genetic study of diverse ancestral populations, in particular Hispanics/Latinos., Author summary Red blood cells (RBC) are important for transport of oxygen to tissues throughout the body. Distribution of RBC traits differs by ethnicity and gender, and both genetic and acquired factors likely contribute to these differences. Prior genetic studies have identified physical regions of the genome associated with RBC traits in populations with European, African, and Asian ancestry. These studies have not included individuals with ancestry from the American continents (Amerindian ancestry), such as Hispanics/Latinos. In an analysis of RBC traits in up to 19,608 Hispanics/Latinos, we identified an Amerindian-ancestry genetic association in a known alpha-globin regulatory region. We also identified three new RBC trait associations, including a regulatory variant of SLC12A2 that encodes a RBC membrane ion-transport protein. Experimental disruption of this regulatory element led to reduced expression of both SLC12A2 and an adjacent long non-coding RNA in human erythroid progenitor cells. These results contribute to understanding the physiology of red blood cells and reinforce the importance of genetic study of diverse ancestry populations, in particular Hispanics/Latinos.

Introduction: Mean corpuscular volume (MCV) of red blood cells (RBCs) is a heritable index commonly used in clinical workup of anemia. Macrocytosis, or high MCV, is associated with cardiovascular events in chronic kidney disease patients and all-cause mortality. Genome-wide association studies (GWAS) of MCV have been conducted primarily in European and Asian populations; few have included populations of African or American ancestry. The Multiethnic Genotyping Array (MEGA) was designed to improve variant discovery and fine-mapping in US minorities by better capturing population-specific genetic variation. To identify novel MCV loci and examine evidence of generalization of previously reported MCV loci to populations with African and Amerindian genetic admixture, we conducted a GWAS of African American and Hispanic/Latino Population Architecture using Genomics and Epidemiology (PAGE II) participants on the MEGA array. Methods: We employed Illumina MEGA genotype data to evaluate the association between ~1.5 million SNPs and MCV. Generalized estimating equation models were adjusted for age, sex, current smoking status, study, study center, and the top 10 study-specific principal components of genetic ancestry. Race/ethnic-stratified analyses were combined using inverse-variance weighted fixed-effects meta-analysis to evaluate the entire study sample. Significance thresholds of 1.8x10 -3 and 5x10 -8 were used to define generalization and genome-wide significance, respectively. Results: The study population of 3,734 African Americans and 15,505 Hispanic/Latinos was 62% female with a mean age of 50 (range: 18 to 94 years); 19% of participants were current smokers. Trans-ethnic meta-analysis identified 12 genome-wide significant loci for MCV, including two associations at previously unreported loci: FIGNL1/IKZF1 (chromosome 7p12.2, coded allele frequency [CAF] range=51 to 57%), and LRP6 (chromosome 12p13.2, CAF range=0.3 to 1.7%). FIGNL1 is involved in DNA double-strand-break repair, and IKZF1 is a zinc-finger protein that functions in the hemo-lymphopoetic system. LRP6 encodes an LDL receptor involved in the Wnt/beta-catenin signaling cascade. Ninety-three percent of previously reported MCV loci generalized to the combined PAGE study population, ten at genome-wide significant levels. No previously unreported MCV loci were detected in race/ethnic stratified analyses. Conclusion: Generalization of previously reported MCV loci to African Americans and Hispanic/Latinos highlights the shared genetic architecture of MCV. The potentially novel associations identified at chromosomes 7p12.2 and 12p13.2 also underscore the benefits of performing GWAS in ancestrally diverse populations using arrays that better capture global genetic variation.

Circulating white blood cell (WBC) counts (neutrophils, monocytes, lymphocytes, eosinophils, basophils) differ by ethnicity. The genetic factors underlying basal WBC traits in Hispanics/Latinos are unknown. We performed a genome-wide association study of total WBC and differential counts in a large, ethnically diverse US population sample of Hispanics/Latinos ascertained by the Hispanic Community Health Study and Study of Latinos (HCHS/SOL). We demonstrate that several previously known WBC-associated genetic loci (e.g. the African Duffy antigen receptor for chemokines null variant for neutrophil count) are generalizable to WBC traits in Hispanics/Latinos. We identified and replicated common and rare germ-line variants at FLT3 (a gene often somatically mutated in leukemia) associated with monocyte count. The common FLT3 variant rs76428106 has a large allele frequency differential between African and non-African populations. We also identified several novel genetic loci involving or regulating hematopoietic transcription factors (CEBPE-SLC7A7, CEBPA and CRBN-TRNT1) associated with basophil count. The minor allele of the CEBPE variant associated with lower basophil count has been previously associated with Amerindian ancestry and higher risk of acute lymphoblastic leukemia in Hispanics. Together, these data suggest that germline genetic variation affecting transcriptional and signaling pathways that underlie WBC development and lineage specification can contribute to inter-individual as well as ethnic differences in peripheral blood cell counts (normal hematopoiesis) in addition to susceptibility to leukemia (malignant hematopoiesis).

Introduction: Sudden cardiac death accounts for 150,000 to 400,000 deaths in the United States annually. Published studies of sudden death use restrictive inclusion criteria such as timing of death, death-certificate disease attribution, hospital transport, attempted resuscitation, and victim survival status. These case definitions may undercount minority victims, victims who are socially isolated, or victims with limited healthcare access, leading to mischaracterization of sudden death in the general population. The SUDDEN study is a population-based epidemiologic analysis of out-of-hospital sudden unexpected death (OHSUD) in North Carolina that aims to characterize OHSUD incidence in the general population and identify associated risk factors. Methods: Wake County, North Carolina, is a predominantly urban county with approximately 4,700 adult deaths in a 2013 population of 974,289. All possible sudden unexpected deaths over one year in Wake County were collected via EMS. Death certificates, medical records, and post mortem examination data were used to determine demographic information and comorbidities. Of 1,138 18- to 64 year olds identified as possible OHSUD victims, 187 cases were adjudicated into the study population. Population-based estimates were age- and sex-standardized to 2013 North Carolina mortality tables to estimate a statewide OHSUD count. Results: Out-of-hospital sudden unexpected death accounted for approximately 14% of all deaths in adults under age 65. Up to 13.5% of all 2013 Wake County deaths outside of a healthcare institution were attributable to OHSUD. Approximately 2/3 of SUDDEN cases would have been excluded by timing criteria in commonly used sudden death case definitions. Hypertension, ischemic heart disease, hypercholesterolemia, COPD, and diabetes were all highly prevalent in Wake County OHSUD victims. OHSUD accounted for 48% of deaths in 18- to 64-year-olds with diseases of the heart, stroke/cerebrovascular disease, or chronic lower respiratory disease listed as the cause of death. 52% of all 2013 OHSUD in Wake County listed a cardiac primary cause on the death certificate; 37% were listed as having non-cardiac causes, with the remaining listing an unknown or a non-natural cause (e.g., trauma). Excluding non-cardiac causes of death would misclassify over 1,500 OHSUD victims per year in North Carolina alone. Discussion: Sudden death incidence estimates vary widely according to published exclusion criteria, leading to a mischaracterization of a disease that needs to be addressed on a national level. Restrictive exclusion criteria used in published studies of sudden death underestimate the incidence of OHSUD and divert resources from populations that would benefit the most from targeted interventions.

Introduction: Variability within the normal population range of hematocrit is associated with stroke and myocardial infarction. Published GWAS of hematocrit have identified multiple loci, yet few studies have included populations of Hispanic or African descent, thereby limiting opportunities to identify population-specific variants or narrow associated regions for functional analysis. We present a fine-mapping analysis of six previously identified hematocrit loci in African American and Hispanic/Latino participants of the PAGE study. Methods: Approximately 200,000 genotyped or imputed Metabochip variants were examined for association with hematocrit (proportion of whole blood comprising red blood cells) in 19,822 Hispanic/Latino and 19,973 African American participants. SNPs were excluded on a population-specific basis if effective heterozygosity was < 30. Primary and conditional analyses were performed in Plink, ProbABEL, or SuGen; fixed-effects meta-analyses were performed in Metal. Trans-ethnic and ancestry-specific meta-analyses were performed in MANTRA to generate 99% credible intervals for previously published variants that generalized to our populations. Results: We first examined whether 8,261 variants in five previously identified hematocrit loci ( HFE , ABO , HK1 , SH2B3 / ATXN2 , and TMPRSS6 ) were associated with hematocrit in our study populations. Three loci generalized (p-4 ) to Hispanic/Latino participants ( ABO, HK1, and TMPRSS6 ) and three generalized to African Americans ( HFE , ABO , and SH2B3/ATXN2 ). Among generalized loci, conditional analyses adjusting for published variants in European-ancestry or East Asian populations did not identify any independently associated SNPs in Hispanic Latinos or African Americans (p-5 ). Trans-ethnic meta-analysis for the ABO locus resulted in a 5 SNP, 13kb 99% credible interval, shorter than both the Hispanic/Latino (17kb) and African American (360kb) credible intervals. In discovery analysis, we identified one variant associated with hematocrit in Hispanic/Latinos at array-wide significance levels ( PROX1 locus, p-7 ). No novel loci were identified in African Americans. Conclusion: Our findings provide evidence that the same genomic loci influence normal variation in hematocrit values across diverse ancestral populations. Trans-ethnic fine mapping of the gene-rich ABO locus—which has been associated with ischemic stroke, thrombosis, and myocardial infarction in addition to hematocrit GWA studies—suggests that a functional variant may reside in the first intron of the ABO coding region. Additionally, identification of previously unidentified associations in Hispanic/Latinos emphasizes the importance of including diverse populations in association studies as well as the potential to identify population-specific functional variants within known or discovery loci.

Platelets play an essential role in hemostasis and thrombosis. We performed a genome-wide association study of platelet count in 12,491 participants of the Hispanic Community Health Study/Study of Latinos by using a mixed-model method that accounts for admixture and family relationships. We discovered and replicated associations with five genes (ACTN1, ETV7, GABBR1-MOG, MEF2C, and ZBTB9-BAK1). Our strongest association was with Amerindian-specific variant rs117672662 (p value = 1.16× 10(-28)) in ACTN1, agene implicated in congenital macrothrombocytopenia. rs117672662 exhibited allelic differences in transcriptional activity and protein binding in hematopoietic cells. Our results underscore the value of diverse populations to extend insights into the allelic architecture of complex traits.

The transcription factor SOX10 has essential roles in neural crest-derived cell populations, including myelinating Schwann cells—specialized glial cells responsible for ensheathing axons in the peripheral nervous system. Importantly, SOX10 directly regulates the expression of genes essential for proper myelin function. To date, only a handful of SOX10 target loci have been characterized in Schwann cells. Addressing this lack of knowledge will provide a better understanding of Schwann cell biology and candidate loci for relevant diseases such as demyelinating peripheral neuropathies. We have identified a highly-conserved SOX10 binding site within an alternative promoter at the SH3-domain kinase binding protein 1 (Sh3kbp1) locus. The genomic segment identified at Sh3kbp1 binds to SOX10 and displays strong promoter activity in Schwann cells in vitro and in vivo. Mutation of the SOX10 binding site ablates promoter activity, and ectopic expression of SOX10 in SOX10-negative cells promotes the expression of endogenous Sh3kbp1. Combined, these data reveal Sh3kbp1 as a novel target of SOX10 and raise important questions regarding the function of SH3KBP1 isoforms in Schwann cells.

Background Mesenchymal stromal cells have been recently isolated from thymus gland tissue discarded after surgical procedures. The role of this novel cell type in heart regeneration has yet to be defined. The purpose of this study was to evaluate the therapeutic potential of human thymus-derived mesenchymal stromal cells using self-organized cardiac tissue as an in vitro platform for quantitative assessment. Methods Mesenchymal stromal cells were isolated from discarded thymus tissue from neonates undergoing heart surgery and were incubated in differentiation media to demonstrate multipotency. Neonatal rat cardiomyocytes self-organized into cardiac tissue fibers in a custom culture dish either alone or in combination with varying numbers of mesenchymal stromal cells. A transducer measured force generated by spontaneously contracting self-organized cardiac tissue fibers. Work and power outputs were calculated from force tracings. Immunofluorescence was performed to determine the fate of the thymus-derived mesenchymal stromal cells. Results Mesenchymal stromal cells were successfully isolated from discarded thymus tissue. After incubation in differentiation media, mesenchymal stromal cells attained the expected phenotypes. Although mesenchymal stromal cells did not differentiate into mature cardiomyocytes, addition of these cells increased the rate of fiber formation, force production, and work and power outputs. Self-organized cardiac tissue containing mesenchymal stromal cells acquired a defined microscopic architecture. Conclusions Discarded thymus tissue contains mesenchymal stromal cells, which can augment force production and work and power outputs of self-organized cardiac tissue fibers by several-fold. These findings indicate the potential utility of mesenchymal stromal cells in treating heart failure.

Background: The World Health Organization (WHO) criteria for sudden unexpected death (SUD) describes either witnessed death or unwitnessed death occurring no more than 24 hours after the subject was last seen alive. Sudden Unexpected Death in North Carolina (SUDDEN) is a population-based study of out-of-hospital unexpected deaths using a clinical definition of sudden death without restrictions on the timing of death. Use of time-limiting definitions was found to underestimate the incidence of SUD by 4 to 19 fold. We sought to explore the characteristics and risk factors of this underestimated population. Methods: From March 2013 to March 2014, all cases of possible sudden unexpected death were identified from electronic query of the Wake County EMS database. Expected deaths were excluded and independent cardiologists made determination for cohort inclusion by review of medical history. We stratified the cohort by timing of death (less than 24 hours versus above 24 hours and unknown) and analyzed the observed difference in incidence between the two groups. Results: Out of 1138 total EMS referrals, 190 were adjudicated as SUD cases into the final cohort. SUDDEN captured 70% more people than studies that used WHO-based criteria. In comparison with cases excluded by WHO criteria, cases meeting WHO criteria were younger (50.1 ± 9.8 years 54.5 ± 8.2 years, p Conclusion: Time-limiting definitions underestimate SUD incidence. Preliminary study shows trends in some demographic and clinical characteristics between SUDDEN and WHO-recommended criteria. An expanded study sample of a population-based cohort similar to SUDDEN is needed to understand the differences in this often misclassified population.

Background: The biology of platelets_anuclear megakaryocyte fragments required for clotting_is well defined, with abnormalities resulting in clotting disorders ranging from asymptomatic to severe. Although platelet count (PLT) is highly heritable (h 2 =0.57 in NHANES), genetic regulation of this trait remains incompletely characterized. In particular, Hispanic Latinos are a diverse population in which the genetic variability of PLT has not been analyzed. We aimed to 1) identify novel loci associated with PLT in participants of HCHS/SoL; and 2) determine if previously identified PLT loci generalize to the Hispanic Latino population. Methods: We performed a genome-wide association study of PLT in 12,491 participants of HCHS/SoL, which includes individuals with Dominican, Puerto Rican, Mexican, Cuban, South American, and Central American ancestral origins residing in the United States. Participants were genotyped on the Illumina SoL Omni2.5M array and imputed to the 1000 Genomes Phase I Reference panel. We implemented linear mixed-model regression assuming an additive genetic model, adjusting for sex, age, study center, ancestry and sample weights, and including random effects for individual relatedness, household, and block group. Results: Ten independent loci were significantly (α = 5x10 -8 ) associated with PLT in HCHS/SoL and 13 of 57 previously identified platelet-count GWAS loci generalized to HCHS/SoL (+/-500kb, α = 8.78x10 -4 ). Discovery loci included a significant association near GABBR1 (rs3131857, MAF = 40%, β = -0.16) and a suggestive association near ETV7 (rs9470264, MAF = 20%, β = -0.19). Furthermore, a noncoding variant in ACTN1 was associated with increased PLT (rs117672662, MAF = 6%, β = 0.61); ACTN1 codes for alpha-actinin, a multi-isoform actin-binding protein involved in cytoskeleton organization and platelet/megakaryocyte structural integrity. Missense mutations in ACTN1 were recently implicated in congenital thrombocytopenia; exome sequencing also associated this locus with PLT, which supports a potential functional role in PLT for this gene. Conditional regression analyses to assess secondary association signals are in progress. Conclusions: Genetic associations in two discovery loci ( ETV7 and GABBR1 ) underscore the benefit of using diverse populations in GWAS. Expanding the number of loci associated with platelet count will help elucidate disease mechanisms and develop approaches to treat platelet disorders in populations of all ancestries.

Background Polytetrafluoroethylene (PTFE) and porcine small intestinal submucosa (pSIS) are patch materials used in congenital heart surgery. Porcine SIS is an extracellular-matrix scaffold that may interact with stem or progenitor cells. To evaluate this, we determined the in vitro effects of pSIS and PTFE on human bone marrow mesenchymal stromal cells (MSCs) and cardiac progenitor cells (CPCs) in 3 areas; cell proliferation, angiogenic growth-factor production, and differentiation. Methods Human MSCs and CPCs were seeded onto pSIS and PTFE patches. Cell-seeded patches were cultured and then assessed for cell viability and proliferation and supernatant vascular endothelial growth factor A (VEGFA) levels. Cell proliferation was quantified by MTT assay (3-[4,5-dimethylthiazol-2-yl]-2,5-diphenyltetrazolium bromide). Quantitative real-time polymerase chain reaction was performed on cell-seeded scaffolds to determine relative changes in gene expression related to angiogenesis and cardiogenesis. Results The MSCs and CPCs were able to attach and proliferate on pSIS and PTFE. The proliferation rate of each cell type was similar on pSIS. Total RNA isolation was only possible from the cell-seeded pSIS patches. The MSC VEGFA production was increased by pSIS. Porcine SIS promoted an angiogenic gene profile in MSCs and an early cardiogenic profile in CPCs. Conclusions Both PTFE and pSIS allow for varying degrees of cell proliferation. Porcine SIS elicits different phenotypical responses in MSCs as compared with CPCs, which indicates that pSIS may be a bioactive scaffold that modulates stem cell activation and proliferation. These findings highlight the differences in scaffold material strategies and suggest potential advantages of bioactive approaches.

Background The ERBB3 gene is essential for the proper development of the neural crest (NC) and its derivative populations such as Schwann cells. As with all cell fate decisions, transcriptional regulatory control plays a significant role in the progressive restriction and specification of NC derived lineages during development. However, little is known about the sequences mediating transcriptional regulation of ERBB3 or the factors that bind them. Results In this study we identified three transcriptional enhancers at the ERBB3 locus and evaluated their regulatory potential in vitro in NC-derived cell types and in vivo in transgenic zebrafish. One enhancer, termed ERBB3_MCS6, which lies within the first intron of ERBB3, directs the highest reporter expression in vitro and also demonstrates epigenetic marks consistent with enhancer activity. We identify a consensus SOX10 binding site within ERBB3_MCS6 and demonstrate, in vitro, its necessity and sufficiency for the activity of this enhancer. Additionally, we demonstrate that transcription from the endogenous Erbb3 locus is dependent on Sox10. Further we demonstrate in vitro that Sox10 physically interacts with that ERBB3_MCS6. Consistent with its in vitro activity, we also show that ERBB3_MCS6 drives reporter expression in NC cells and a subset of its derivative lineages in vivo in zebrafish in a manner consistent with erbb3b expression. We also demonstrate, using morpholino analysis, that Sox10 is necessary for ERBB3_MCS6 expression in vivo in zebrafish. Conclusions Taken collectively, our data suggest that ERBB3 may be directly regulated by SOX10, and that this control may in part be facilitated by ERBB3_MCS6.

Background Myelin protein zero (MPZ) is a critical structural component of myelin in the peripheral nervous system. The MPZ gene is regulated, in part, by the transcription factors SOX10 and EGR2. Mutations in MPZ, SOX10, and EGR2 have been implicated in demyelinating peripheral neuropathies, suggesting that components of this transcriptional network are candidates for harboring disease-causing mutations (or otherwise functional variants) that affect MPZ expression. Methodology We utilized a combination of multi-species sequence comparisons, transcription factor-binding site predictions, targeted human DNA re-sequencing, and in vitro and in vivo enhancer assays to study human non-coding MPZ variants. Principal Findings Our efforts revealed a variant within the first intron of MPZ that resides within a previously described SOX10 binding site is associated with decreased enhancer activity, and alters binding of nuclear proteins. Additionally, the genomic segment harboring this variant directs tissue-relevant reporter gene expression in zebrafish. Conclusions This is the first reported MPZ variant within a cis-acting transcriptional regulatory element. While we were unable to implicate this variant in disease onset, our data suggests that similar non-coding sequences should be screened for mutations in patients with neurological disease. Furthermore, our multi-faceted approach for examining the functional significance of non-coding variants can be readily generalized to study other loci important for myelin structure and function.

Cardiac tissue-engineering research is focused on the development of functional three-dimensional (3D) heart muscle in vitro. These models allow the detailed study of critical events in organogenesis, such as the establishment of cell-cell communication and construction and modification of the extracellular matrix. We have previously described a model for 3D heart muscle, termed cardioids, formed by the spontaneous delamination of a cohesive monolayer of primary cells in the absence of any synthetic scaffolding material. In an earlier publication, we have shown that, upon electrical stimulation, cardioids generate a twitch force in the range of 200-300 microN, generate a specific force (twitch force normalized to total cross-sectional area) of 2-4 kN/m(2), and can be electrically paced at frequencies of up to 10 Hz without any notable fatigue. We have two objectives for the current study: model development and model optimization. Our model development efforts are focused on providing additional characterization of the cardioid model. In this study, we show for the first time that cardioids show a pattern of gene expression comparable to that of cells cultured in two dimensions on tissue culture plastic and normal mammalian heart muscle. Compared with primary cardiac cells cultured on tissue culture plastic, the expression of alpha-myosin heavy chain (MHC), beta-MHC, SERCA2, and phospholamban was significantly higher in cardioids. Our second objective, model optimization, is focused on evaluating the effect of several cell culture variables on cardioid formation and function. Specifically, we looked at the effect of plating density (1.0-4.0 x 10(6) cells per cardioid), concentration of two adhesion proteins (laminin at 0.2-2.0 microg/cm(2) and fibronectin at 1-10 microg/cm(2)), myocyte purity (using preplating times of 15 and 60 min), and ascorbic acid stimulation (1-100 microl/ml). For our optimization studies, we utilized twitch force in response to electrical stimulation as our endpoint metric. Based on these studies, we found that cardioids formed with a plating density in the range 3-4 x 10(6) cells per cardioid generated the maximum twitch force, whereas increasing the surface adhesion protein (using either laminin or fibronectin) and increasing the myocyte purity both resulted in a decrease in twitch force. In addition, increasing the ascorbic acid concentration resulted in an increase in the baseline force of cardioids, which was recorded in the absence of electrical stimulation. Based on the model development studies, we have shown that cardioids do indeed exhibit a gene expression pattern similar to normal mammalian heart muscle. This provides further validity for the cardioid model. Based on the model optimization studies, we have identified specific cell culture regimes which support cardioid formation and function. These results are specific to the cardioid model; however, they may be translated and applied to other tissue-engineering models. Collectively, the work described in this study provides insight into the formation of functional 3D heart muscle and the effect of several cell culture variables on tissue formation and function.

Single-cell RNA-seq (scRNA-seq) is a powerful genomics technology to interrogate the cellular composition and behaviors of complex systems. While the number of scRNA-seq datasets and available computational analysis tools have grown exponentially, there are limited systematic data sharing strategies to allow rapid exploration and re-analysis of single-cell datasets, particularly in the cardiovascular field. We previously introduced PlaqView, an open-source web portal for the exploration and analysis of published atherosclerosis single-cell datasets. Now, we introduce PlaqView 2.0 (www.plaqview.com), which provides expanded features and functionalities as well as additional cardiovascular single-cell datasets. We showcase improved PlaqView functionality, backend data processing, user-interface, and capacity. PlaqView brings new or improved tools to explore scRNA-seq data, including gene query, metadata browser, cell identity prediction, ad hoc RNA-trajectory analysis, and drug-gene interaction prediction. PlaqView serves as one of the largest central repositories for cardiovascular single-cell datasets, which now includes data from human aortic aneurysm, gene-specific mouse knockouts, and healthy references. PlaqView 2.0 brings advanced tools and high-performance computing directly to users without the need for any programming knowledge. Lastly, we outline steps to generalize and repurpose PlaqView's framework for single-cell datasets from other fields.

Background Nearly half of adults in the United States who are diagnosed with hypertension use blood-pressure-lowering medications. Yet there is a large interindividual variability in the response to these medications. Two complementary gene–environment interaction methods have been published and incorporated into publicly available software packages to examine interaction effects, including whether genetic variants modify the association between medication use and blood pressure. The first approach uses a gene–environment interaction term to measure the change in outcome when both the genetic marker and medication are present (the “interaction model”). The second approach tests for effect-size differences between strata of an environmental exposure (the “med-diff” approach). However, no studies have quantitatively compared how these methods perform with respect to 1 or 2 degree of freedom (DF) tests or in family-based data sets. We evaluated these 2 approaches using simulated genotype–medication response interactions at 3 single nucleotide polymorphisms (SNPs) across a range of minor allele frequencies (MAFs 0.1–5.4 %) using the Genetic Analysis Workshop 19 family sample. Results The estimated interaction effect sizes were on average larger in the interaction model approach compared to the med-diff approach. The true positive proportion was higher for the med-diff approach for SNPs less than 1 % MAF, but higher for the interaction model when common variants were evaluated (MAF >5 %). The interaction model produced lower false-positive proportions than expected (5 %) across a range of MAFs for both the 1DF and 2DF tests. In contrast, the med-diff approach produced higher but stable false-positive proportions around 5 % across MAFs for both tests. Conclusions Although the 1DF tests both performed similarly for common variants, the interaction model estimated true interaction effects with less bias and higher true positive proportions than the med-diff approach. However, if rare variation (MAF