Single-cell transcriptomic profiling of human pancreatic islets reveals genes responsive to glucose exposure over 24 h.

AIMS/HYPOTHESIS: Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycaemia, beta cell glucotoxicity and subsequently type 2 diabetes. In this study, we explored the effects of in vitro hyperglycaemic conditions on human pancreatic islet gene expression across 24 h in six pancreatic cell types: alpha; beta; gamma; delta; ductal; and acinar. We hypothesised that genes associated with hyperglycaemic conditions may be relevant to the onset and progression of diabetes. METHODS: We exposed human pancreatic islets from two donors to low (2.8 mmol/l) and high (15.0 mmol/l) glucose concentrations over 24 h in vitro. To assess the transcriptome, we performed single-cell RNA-seq (scRNA-seq) at seven time points. We modelled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Additionally, we integrated genomic features and genetic summary statistics to nominate candidate effector genes. For three of these genes, we functionally characterised the effect on insulin production and secretion using CRISPR interference to knock down gene expression in EndoC-ßH1 cells, followed by a glucose-stimulated insulin secretion assay. RESULTS: In the discrete time models, we identified 1344 genes associated with time and 668 genes associated with glucose exposure across all cell types and time points. In the continuous time models, we identified 1311 genes associated with time, 345 genes associated with glucose exposure and 418 genes associated with interaction effects between time and glucose across all cell types. By integrating these expression profiles with summary statistics from genetic association studies, we identified 2449 candidate effector genes for type 2 diabetes, HbA1c, random blood glucose and fasting blood glucose. Of these candidate effector genes, we showed that three (ERO1B, HNRNPA2B1 and RHOBTB3) exhibited an effect on glucose-stimulated insulin production and secretion in EndoC-ßH1 cells. CONCLUSIONS/INTERPRETATION: The findings of our study provide an in-depth characterisation of the 24 h transcriptomic response of human pancreatic islets to glucose exposure at a single-cell resolution. By integrating differentially expressed genes with genetic signals for type 2 diabetes and glucose-related traits, we provide insights into the molecular mechanisms underlying glucose homeostasis. Finally, we provide functional evidence to support the role of three candidate effector genes in insulin secretion and production. DATA AVAILABILITY: The scRNA-seq data from the 24 h glucose exposure experiment performed in this study are available in the database of Genotypes and Phenotypes (dbGap; https://www.ncbi.nlm.nih.gov/gap/ ) with accession no. phs001188.v3.p1. Study metadata and summary statistics for the differential expression, gene set enrichment and candidate effector gene prediction analyses are available in the Zenodo data repository ( https://zenodo.org/ ) under accession number 11123248. The code used in this study is publicly available at https://github.com/CollinsLabBioComp/publication-islet_glucose_timecourse .

Enhancing Human Health and Wellbeing through Sustainably and Equitably Unlocking a Healthy Ocean's Potential.

A healthy ocean is essential for human health, and yet the links between the ocean and human health are often overlooked. By providing new medicines, technologies, energy, foods, recreation, and inspiration, the ocean has the potential to enhance human health and wellbeing. However, climate change, pollution, biodiversity loss, and inequity threaten both ocean and human health. Sustainable realisation of the ocean's health benefits will require overcoming these challenges through equitable partnerships, enforcement of laws and treaties, robust monitoring, and use of metrics that assess both the ocean's natural capital and human wellbeing. Achieving this will require an explicit focus on human rights, equity, sustainability, and social justice. In addition to highlighting the potential unique role of the healthcare sector, we offer science-based recommendations to protect both ocean health and human health, and we highlight the unique potential of the healthcare sector tolead this effort.

Generation of Human Isogenic Induced Pluripotent Stem Cell Lines with CRISPR Prime Editing.

We developed an efficient CRISPR prime editing protocol and generated isogenic-induced pluripotent stem cell (iPSC) lines carrying heterozygous or homozygous alleles for putatively causal single nucleotide variants at six type 2 diabetes loci (ABCC8, MTNR1B, TCF7L2, HNF4A, CAMK1D, and GCK). Our two-step sequence-based approach to first identify transfected cell pools with the highest fraction of edited cells significantly reduced the downstream efforts to isolate single clones of edited cells. We found that prime editing can make targeted genetic changes in iPSC and optimization of system components and guide RNA designs that were critical to achieve acceptable efficiency. Systems utilizing PEmax, epegRNA modifications, and MLH1dn provided significant benefit, producing editing efficiencies of 36-73%. Editing success and pegRNA design optimization required for each variant differed depending on the sequence at the target site. With attention to design, prime editing is a promising approach to generate isogenic iPSC lines, enabling the study of specific genetic changes in a common genetic background.

Functional interrogation of twenty type 2 diabetes-associated genes using isogenic human embryonic stem cell-derived ß-like cells.

Genetic studies have identified numerous loci associated with type 2 diabetes (T2D), but the functional roles of many loci remain unexplored. Here, we engineered isogenic knockout human embryonic stem cell lines for 20 genes associated with T2D risk. We examined the impacts of each knockout on ß cell differentiation, functions, and survival. We generated gene expression and chromatin accessibility profiles on ß cells derived from each knockout line. Analyses of T2D-association signals overlapping HNF4A-dependent ATAC peaks identified a likely causal variant at the FAIM2 T2D-association signal. Additionally, the integrative association analyses identified four genes (CP, RNASE1, PCSK1N, and GSTA2) associated with insulin production, and two genes (TAGLN3 and DHRS2) associated with ß cell sensitivity to lipotoxicity. Finally, we leveraged deep ATAC-seq read coverage to assess allele-specific imbalance at variants heterozygous in the parental line and identified a single likely functional variant at each of 23 T2D-association signals.

Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits.

Genetic association studies have identified hundreds of independent signals associated with type 2 diabetes (T2D) and related traits. Despite these successes, the identification of specific causal variants underlying a genetic association signal remains challenging. In this study, we describe a deep learning (DL) method to analyze the impact of sequence variants on enhancers. Focusing on pancreatic islets, a T2D relevant tissue, we show that our model learns islet-specific transcription factor (TF) regulatory patterns and can be used to prioritize candidate causal variants. At 101 genetic signals associated with T2D and related glycemic traits where multiple variants occur in linkage disequilibrium, our method nominates a single causal variant for each association signal, including three variants previously shown to alter reporter activity in islet-relevant cell types. For another signal associated with blood glucose levels, we biochemically test all candidate causal variants from statistical fine-mapping using a pancreatic islet beta cell line and show biochemical evidence of allelic effects on TF binding for the model-prioritized variant. To aid in future research, we publicly distribute our model and islet enhancer perturbation scores across ~67 million genetic variants. We anticipate that DL methods like the one presented in this study will enhance the prioritization of candidate causal variants for functional studies.

Noncoding variants alter GATA2 expression in rhombomere 4 motor neurons and cause dominant hereditary congenital facial paresis.

Hereditary congenital facial paresis type 1 (HCFP1) is an autosomal dominant disorder of absent or limited facial movement that maps to chromosome 3q21-q22 and is hypothesized to result from facial branchial motor neuron (FBMN) maldevelopment. In the present study, we report that HCFP1 results from heterozygous duplications within a neuron-specific GATA2 regulatory region that includes two enhancers and one silencer, and from noncoding single-nucleotide variants (SNVs) within the silencer. Some SNVs impair binding of NR2F1 to the silencer in vitro and in vivo and attenuate in vivo enhancer reporter expression in FBMNs. Gata2 and its effector Gata3 are essential for inner-ear efferent neuron (IEE) but not FBMN development. A humanized HCFP1 mouse model extends Gata2 expression, favors the formation of IEEs over FBMNs and is rescued by conditional loss of Gata3. These findings highlight the importance of temporal gene regulation in development and of noncoding variation in rare mendelian disease.

Single-cell transcriptomic profiling of human pancreatic islets reveals genes responsive to glucose exposure over 24 hours.

Disruption of pancreatic islet function and glucose homeostasis can lead to the development of sustained hyperglycemia, beta cell glucotoxicity, and ultimately type 2 diabetes (T2D). In this study, we sought to explore the effects of hyperglycemia on human pancreatic islet (HPI) gene expression by exposing HPIs from two donors to low (2.8mM) and high (15.0mM) glucose concentrations over 24 hours, assaying the transcriptome at seven time points using single-cell RNA sequencing (scRNA-seq). We modeled time as both a discrete and continuous variable to determine momentary and longitudinal changes in transcription associated with islet time in culture or glucose exposure. Across all cell types, we identified 1,528 genes associated with time, 1,185 genes associated with glucose exposure, and 845 genes associated with interaction effects between time and glucose. We clustered differentially expressed genes across cell types and found 347 modules of genes with similar expression patterns across time and glucose conditions, including two beta cell modules enriched in genes associated with T2D. Finally, by integrating genomic features from this study and genetic summary statistics for T2D and related traits, we nominate 363 candidate effector genes that may underlie genetic associations for T2D and related traits.

Functional interrogation of twenty type 2 diabetes-associated genes using isogenic hESC-derived ß-like cells.

Genetic studies have identified numerous loci associated with type 2 diabetes (T2D), but the functional role of many loci has remained unexplored. In this study, we engineered isogenic knockout human embryonic stem cell (hESC) lines for 20 genes associated with T2D risk. We systematically examined ß-cell differentiation, insulin production and secretion, and survival. We performed RNA-seq and ATAC-seq on hESC-ß cells from each knockout line. Analyses of T2D GWAS signals overlapping with HNF4A-dependent ATAC peaks identified a specific SNP as a likely causal variant. In addition, we performed integrative association analyses and identified four genes ( CP, RNASE1, PCSK1N and GSTA2 ) associated with insulin production, and two genes ( TAGLN3 and DHRS2 ) associated with sensitivity to lipotoxicity. Finally, we leveraged deep ATAC-seq read coverage to assess allele-specific imbalance at variants heterozygous in the parental hESC line, to identify a single likely functional variant at each of 23 T2D GWAS signals.

Human pancreatic islet microRNAs implicated in diabetes and related traits by large-scale genetic analysis.

Genetic studies have identified ≥240 loci associated with the risk of type 2 diabetes (T2D), yet most of these loci lie in non-coding regions, masking the underlying molecular mechanisms. Recent studies investigating mRNA expression in human pancreatic islets have yielded important insights into the molecular drivers of normal islet function and T2D pathophysiology. However, similar studies investigating microRNA (miRNA) expression remain limited. Here, we present data from 63 individuals, the largest sequencing-based analysis of miRNA expression in human islets to date. We characterized the genetic regulation of miRNA expression by decomposing the expression of highly heritable miRNAs into cis- and trans-acting genetic components and mapping cis-acting loci associated with miRNA expression [miRNA-expression quantitative trait loci (eQTLs)]. We found i) 84 heritable miRNAs, primarily regulated by trans-acting genetic effects, and ii) 5 miRNA-eQTLs. We also used several different strategies to identify T2D-associated miRNAs. First, we colocalized miRNA-eQTLs with genetic loci associated with T2D and multiple glycemic traits, identifying one miRNA, miR-1908, that shares genetic signals for blood glucose and glycated hemoglobin (HbA1c). Next, we intersected miRNA seed regions and predicted target sites with credible set SNPs associated with T2D and glycemic traits and found 32 miRNAs that may have altered binding and function due to disrupted seed regions. Finally, we performed differential expression analysis and identified 14 miRNAs associated with T2D status-including miR-187-3p, miR-21-5p, miR-668, and miR-199b-5p-and 4 miRNAs associated with a polygenic score for HbA1c levels-miR-216a, miR-25, miR-30a-3p, and miR-30a-5p.

Cannabis use is associated with low plasma endocannabinoid Anandamide in individuals with psychosis.

BACKGROUND: Cannabis use suppresses the endocannabinoid system in healthy individuals. However, the association between cannabis use with the endocannabinoid system is understudied in individuals with psychosis despite the high rate of cannabis use in these individuals. METHODS: We enrolled 83 individuals who were admitted to an inpatient psychiatric unit with psychotic presentations, and measured their plasma levels of main endocannabinoids, Anandamide (AEA) and 2-Acylglycerol (2-AG), and endocannabinoid related compounds, Palmitoylethanolamine, and N-oleoylethanolamine. Cannabis use was assessed with urine toxicology and frequency of cannabis use was assessed using self-reported questionnaires. The Positive and Negative Syndrome Scale was used to assess the severity of psychotic symptoms. RESULTS: Overall, we had 38 individuals in cannabis positive group (CN+) and 45 individuals in cannabis negative group (CN-). Compared to CN-, CN+ group had lower plasma levels of AEA, which remained significant after controlling for age, gender, race/ethnicity, and use of other drugs. CONCLUSION: Cannabis use is associated with low plasma AEA levels in individuals with psychosis, which is in the same line with reported suppressive effects of cannabis on the endocannabinoid system in healthy individuals. Further studies are needed to investigate the clinical significance of this finding.

Cannabinoid use in psychotic patients impacts inflammatory levels and their association with psychosis severity.

Inflammatory abnormalities are well-documented in individuals with chronic psychotic disorders. Particular attention has focused on interleukin-6 (IL-6) and its correlation with psychotic symptom severity. Cannabis use is associated with an increased risk of psychosis and also has immunomodulating properties. It has been hypothesized that inflammatory disturbances are a common underlying pathology between cannabis use and psychosis. We measured inflammatory markers in individuals admitted to a psychiatric unit with acute psychosis who had toxicology positive for natural and/or synthetic cannabinoids (n = 59) compared to patients with negative cannabinoid toxicology (n = 60). Psychosis severity was assessed using the Positive and Negative Syndrome Scale (PANSS). While PANSS scores were similar between groups, cannabinoid-positive participants were more likely to receive pro re nata (PRN or as-needed) medications for agitation in the psychiatric emergency room, particularly synthetic cannabinoid-positive participants. In unadjusted models, cannabinoid-positive participants had lower interferon-γ (IFN-γ) levels (p = 0.046), but this finding was not significant after adjusting for covariates and multiple comparisons. Among cannabinoid-positive participants, IL-6 levels negatively correlated with PANSS total score (p = 0.040), as well as positive (p = 0.035) and negative (p = 0.024) subscales. Results suggest inflammatory alterations among psychotic individuals with comorbid cannabinoid use.

Single-cell transcriptomics from human pancreatic islets: sample preparation matters.

Single-cell RNA sequencing (scRNA-seq) of human primary tissues is a rapidly emerging tool for investigating human health and disease at the molecular level. However, optimal processing of solid tissues presents a number of technical and logistical challenges, especially for tissues that are only available at autopsy, which includes pancreatic islets, a tissue that is highly relevant to diabetes. To assess the possible effects of different sample preparation protocols on fresh islet samples, we performed a detailed comparison of scRNA-seq data generated with islets isolated from a human donor but processed according to four treatment strategies, including fixation and cryopreservation. We found significant and reproducible differences in the proportion of cell types identified, and more minor effects on cell-specific patterns of gene expression. Fresh islets from a second donor confirmed gene expression signatures of alpha and beta subclusters. These findings may well apply to other tissues, emphasizing the need for careful consideration when choosing processing methods, comparing results between different studies, and/or interpreting data in the context of multiple cell types from preserved tissue.

Integrative analysis of gene expression, DNA methylation, physiological traits, and genetic variation in human skeletal muscle.

We integrate comeasured gene expression and DNA methylation (DNAme) in 265 human skeletal muscle biopsies from the FUSION study with >7 million genetic variants and eight physiological traits: height, waist, weight, waist-hip ratio, body mass index, fasting serum insulin, fasting plasma glucose, and type 2 diabetes. We find hundreds of genes and DNAme sites associated with fasting insulin, waist, and body mass index, as well as thousands of DNAme sites associated with gene expression (eQTM). We find that controlling for heterogeneity in tissue/muscle fiber type reduces the number of physiological trait associations, and that long-range eQTMs (>1 Mb) are reduced when controlling for tissue/muscle fiber type or latent factors. We map genetic regulators (quantitative trait loci; QTLs) of expression (eQTLs) and DNAme (mQTLs). Using Mendelian randomization (MR) and mediation techniques, we leverage these genetic maps to predict 213 causal relationships between expression and DNAme, approximately two-thirds of which predict methylation to causally influence expression. We use MR to integrate FUSION mQTLs, FUSION eQTLs, and GTEx eQTLs for 48 tissues with genetic associations for 534 diseases and quantitative traits. We identify hundreds of genes and thousands of DNAme sites that may drive the reported disease/quantitative trait genetic associations. We identify 300 gene expression MR associations that are present in both FUSION and GTEx skeletal muscle and that show stronger evidence of MR association in skeletal muscle than other tissues, which may partially reflect differences in power across tissues. As one example, we find that increased RXRA muscle expression may decrease lean tissue mass.

Hydroa vacciniforme-like lymphoproliferative disorder: an EBV disease with a low risk of systemic illness in whites.

Patients with classic hydroa vacciniforme-like lymphoproliferative disorder (HVLPD) typically have high levels of Epstein-Barr virus (EBV) DNA in T cells and/or natural killer (NK) cells in blood and skin lesions induced by sun exposure that are infiltrated with EBV-infected lymphocytes. HVLPD is very rare in the United States and Europe but more common in Asia and South America. The disease can progress to a systemic form that may result in fatal lymphoma. We report our 11-year experience with 16 HVLPD patients from the United States and England and found that whites were less likely to develop systemic EBV disease (1/10) than nonwhites (5/6). All (10/10) of the white patients were generally in good health at last follow-up, while two-thirds (4/6) of the nonwhite patients required hematopoietic stem cell transplantation. Nonwhite patients had later age of onset of HVLPD than white patients (median age, 8 vs 5 years) and higher levels of EBV DNA (median, 1 515 000 vs 250 000 copies/ml) and more often had low numbers of NK cells (83% vs 50% of patients) and T-cell clones in the blood (83% vs 30% of patients). RNA-sequencing analysis of an HVLPD skin lesion in a white patient compared with his normal skin showed increased expression of interferon-γ and chemokines that attract T cells and NK cells. Thus, white patients with HVLPD were less likely to have systemic disease with EBV and had a much better prognosis than nonwhite patients. This trial was registered at www.clinicaltrials.gov as #NCT00369421 and #NCT00032513.

A defect in myoblast fusion underlies Carey-Fineman-Ziter syndrome.

Multinucleate cellular syncytial formation is a hallmark of skeletal muscle differentiation. Myomaker, encoded by Mymk (Tmem8c), is a well-conserved plasma membrane protein required for myoblast fusion to form multinucleated myotubes in mouse, chick, and zebrafish. Here, we report that autosomal recessive mutations in MYMK (OMIM 615345) cause Carey-Fineman-Ziter syndrome in humans (CFZS; OMIM 254940) by reducing but not eliminating MYMK function. We characterize MYMK-CFZS as a congenital myopathy with marked facial weakness and additional clinical and pathologic features that distinguish it from other congenital neuromuscular syndromes. We show that a heterologous cell fusion assay in vitro and allelic complementation experiments in mymk knockdown and mymkinsT/insT zebrafish in vivo can differentiate between MYMK wild type, hypomorphic and null alleles. Collectively, these data establish that MYMK activity is necessary for normal muscle development and maintenance in humans, and expand the spectrum of congenital myopathies to include cell-cell fusion deficits.

Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension.

High blood pressure is a major risk factor for cardiovascular disease and premature death. However, there is limited knowledge on specific causal genes and pathways. To better understand the genetics of blood pressure, we genotyped 242,296 rare, low-frequency and common genetic variants in up to 192,763 individuals and used ∼155,063 samples for independent replication. We identified 30 new blood pressure- or hypertension-associated genetic regions in the general population, including 3 rare missense variants in RBM47, COL21A1 and RRAS with larger effects (>1.5 mm Hg/allele) than common variants. Multiple rare nonsense and missense variant associations were found in A2ML1, and a low-frequency nonsense variant in ENPEP was identified. Our data extend the spectrum of allelic variation underlying blood pressure traits and hypertension, provide new insights into the pathophysiology of hypertension and indicate new targets for clinical intervention.

Re-sequencing expands our understanding of the phenotypic impact of variants at GWAS loci.

Genome-wide association studies (GWAS) have identified >500 common variants associated with quantitative metabolic traits, but in aggregate such variants explain at most 20-30% of the heritable component of population variation in these traits. To further investigate the impact of genotypic variation on metabolic traits, we conducted re-sequencing studies in >6,000 members of a Finnish population cohort (The Northern Finland Birth Cohort of 1966 [NFBC]) and a type 2 diabetes case-control sample (The Finland-United States Investigation of NIDDM Genetics [FUSION] study). By sequencing the coding sequence and 5' and 3' untranslated regions of 78 genes at 17 GWAS loci associated with one or more of six metabolic traits (serum levels of fasting HDL-C, LDL-C, total cholesterol, triglycerides, plasma glucose, and insulin), and conducting both single-variant and gene-level association tests, we obtained a more complete understanding of phenotype-genotype associations at eight of these loci. At all eight of these loci, the identification of new associations provides significant evidence for multiple genetic signals to one or more phenotypes, and at two loci, in the genes ABCA1 and CETP, we found significant gene-level evidence of association to non-synonymous variants with MAF<1%. Additionally, two potentially deleterious variants that demonstrated significant associations (rs138726309, a missense variant in G6PC2, and rs28933094, a missense variant in LIPC) were considerably more common in these Finnish samples than in European reference populations, supporting our prior hypothesis that deleterious variants could attain high frequencies in this isolated population, likely due to the effects of population bottlenecks. Our results highlight the value of large, well-phenotyped samples for rare-variant association analysis, and the challenge of evaluating the phenotypic impact of such variants.

Impact of type 2 diabetes susceptibility variants on quantitative glycemic traits reveals mechanistic heterogeneity.

Patients with established type 2 diabetes display both ß-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.

Autosomal dominant diabetes arising from a Wolfram syndrome 1 mutation.

We used an unbiased genome-wide approach to identify exonic variants segregating with diabetes in a multigenerational Finnish family. At least eight members of this family presented with diabetes with age of diagnosis ranging from 18 to 51 years and a pattern suggesting autosomal dominant inheritance. We sequenced the exomes of four affected members of this family and performed follow-up genotyping of additional affected and unaffected family members. We uncovered a novel nonsynonymous variant (p.Trp314Arg) in the Wolfram syndrome 1 (WFS1) gene that segregates completely with the diabetic phenotype. Multipoint parametric linkage analysis with 13 members of this family identified a single linkage signal with maximum logarithm of odds score 3.01 at 4p16.2-p16.1, corresponding to a region harboring the WFS1 locus. Functional studies demonstrate a role for this variant in endoplasmic reticulum stress, which is consistent with the ß-cell failure phenotype seen in mutation carriers. This represents the first compelling report of a mutation in WFS1 associated with dominantly inherited nonsyndromic adult-onset diabetes.

Trans-ethnic fine-mapping of lipid loci identifies population-specific signals and allelic heterogeneity that increases the trait variance explained.

Genome-wide association studies (GWAS) have identified ~100 loci associated with blood lipid levels, but much of the trait heritability remains unexplained, and at most loci the identities of the trait-influencing variants remain unknown. We conducted a trans-ethnic fine-mapping study at 18, 22, and 18 GWAS loci on the Metabochip for their association with triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), and low-density lipoprotein cholesterol (LDL-C), respectively, in individuals of African American (n = 6,832), East Asian (n = 9,449), and European (n = 10,829) ancestry. We aimed to identify the variants with strongest association at each locus, identify additional and population-specific signals, refine association signals, and assess the relative significance of previously described functional variants. Among the 58 loci, 33 exhibited evidence of association at P<1 × 10(-4) in at least one ancestry group. Sequential conditional analyses revealed that ten, nine, and four loci in African Americans, Europeans, and East Asians, respectively, exhibited two or more signals. At these loci, accounting for all signals led to a 1.3- to 1.8-fold increase in the explained phenotypic variance compared to the strongest signals. Distinct signals across ancestry groups were identified at PCSK9 and APOA5. Trans-ethnic analyses narrowed the signals to smaller sets of variants at GCKR, PPP1R3B, ABO, LCAT, and ABCA1. Of 27 variants reported previously to have functional effects, 74% exhibited the strongest association at the respective signal. In conclusion, trans-ethnic high-density genotyping and analysis confirm the presence of allelic heterogeneity, allow the identification of population-specific variants, and limit the number of candidate SNPs for functional studies.