Trans-Omics For Precision Medicine Research Articles

Epigenetic and proteomic signatures associate with clonal hematopoiesis expansion rate.

Clonal hematopoiesis of indeterminate potential (CHIP), whereby somatic mutations in hematopoietic stem cells confer a selective advantage and drive clonal expansion, not only correlates with age but also confers increased risk of morbidity and mortality. Here, we leverage genetically predicted traits to identify factors that determine CHIP clonal expansion rate. We used the passenger-approximated clonal expansion rate method to quantify the clonal expansion rate for 4,370 individuals in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) cohort and calculated polygenic risk scores for DNA methylation aging, inflammation-related measures and circulating protein levels. Clonal expansion rate was significantly associated with both genetically predicted and measured epigenetic clocks. No associations were identified with inflammation-related lab values or diseases and CHIP expansion rate overall. A proteome-wide search identified predicted circulating levels of myeloid zinc finger 1 and anti-Müllerian hormone as associated with an increased CHIP clonal expansion rate and tissue inhibitor of metalloproteinase 1 and glycine N-methyltransferase as associated with decreased CHIP clonal expansion rate. Together, our findings identify epigenetic and proteomic patterns associated with the rate of hematopoietic clonal expansion.

Whole genome sequencing based analysis of inflammation biomarkers in the Trans-Omics for Precision Medicine (TOPMed) consortium.

Inflammation biomarkers can provide valuable insight into the role of inflammatory processes in many diseases and conditions. Sequencing based analyses of such biomarkers can also serve as an exemplar of the genetic architecture of quantitative traits. To evaluate the biological insight, which can be provided by a multi-ancestry, whole-genome based association study, we performed a comprehensive analysis of 21 inflammation biomarkers from up to 38 465 individuals with whole-genome sequencing from the Trans-Omics for Precision Medicine (TOPMed) program (with varying sample size by trait, where the minimum sample size was n = 737 for MMP-1). We identified 22 distinct single-variant associations across 6 traits-E-selectin, intercellular adhesion molecule 1, interleukin-6, lipoprotein-associated phospholipase A2 activity and mass, and P-selectin-that remained significant after conditioning on previously identified associations for these inflammatory biomarkers. We further expanded upon known biomarker associations by pairing the single-variant analysis with a rare variant set-based analysis that further identified 19 significant rare variant set-based associations with 5 traits. These signals were distinct from both significant single variant association signals within TOPMed and genetic signals observed in prior studies, demonstrating the complementary value of performing both single and rare variant analyses when analyzing quantitative traits. We also confirm several previously reported signals from semi-quantitative proteomics platforms. Many of these signals demonstrate the extensive allelic heterogeneity and ancestry-differentiated variant-trait associations common for inflammation biomarkers, a characteristic we hypothesize will be increasingly observed with well-powered, large-scale analyses of complex traits.

Imputation accuracy across global human populations

Genotype imputation is now fundamental for genome-wide association studies but lacks fairness due to the underrepresentation of references from non-European ancestries. The state-of-the-art imputation reference panel released by the Trans-Omics for Precision Medicine (TOPMed) initiative improved the imputation of admixed African-ancestry and Hispanic/Latino samples, but imputation for populations primarily residing outside of North America may still fall short in performance due to persisting underrepresentation. To illustrate this point, we imputed the genotypes of over 43,000 individuals across 123 populations around the world and identified numerous populations where imputation accuracy paled in comparison to that of European-ancestry populations. For instance, the mean imputation r-squared (Rsq) for variants with minor allele frequencies between 1% and 5% in Saudi Arabians (n= 1,061), Vietnamese (n= 1,264), Thai (n= 2,435), and Papua New Guineans (n= 776) were 0.79, 0.78, 0.76, and 0.62, respectively, compared to 0.90-0.93 for comparable European populations matched in sample size and SNP array content. Outside of Africa and Latin America, Rsq appeared to decrease as genetic distances to European-ancestry reference increased, as predicted. Using sequencing data as ground truth, we also showed that Rsq may over-estimate imputation accuracy for non-European populations more than European populations, suggesting further disparity in accuracy between populations. Using 1,496 sequenced individuals from Taiwan Biobank as a second reference panel to TOPMed, we also assessed a strategy to improve imputation for non-European populations with meta-imputation, but this design did not improve accuracy across frequency spectra. Taken together, our analyses suggest that we must ultimately strive to increase diversity and size to promote equity within genetics research.

Abstract P311: Metabolomic Associations With Blood Pressure and Hypertension in Multi-Ethnic Populations

Introduction: Hypertension (HTN) is a leading cause of cardiovascular disease and premature death. A few circulating metabolites have been associated with blood pressure or HTN; however, findings from multi-ethnic populations are limited. Methods: Discovery analysis included eight population-based cohorts from the Trans-Omics for Precision Medicine (TOPMed) consortium, including adults of White, Black and Hispanic/Latino ancestries. Replication analyses were performed in six independent studies from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) and TOPMed. Fixed-effect inverse variance weighted meta-analysis was applied, on study- and ethnic-specific linear and logistic regression results, to estimate circulating metabolites association with systolic (SBP), diastolic blood pressure (DBP), and HTN cross-sectionally, adjusting for age, sex, body mass index, smoking status, lipids, kidney function, and diabetes prevalence. Global metabolomic analysis and pathway enrichment were performed to identify potentially altered metabolic pathways, and two-sample Mendelian randomization (MR) was performed to test for causal relationships between replicated metabolites and SBP, DBP, and HTN. Results: Out of 1,055 meta-analyzed metabolites, 336 were significantly associated (FDR&lt0.05) with all three BP traits among 17,767 participants (7,097 White, 5,406 Black, and 5,264 Hispanic/Latino). Xanthopterin had the strongest positive association (SBP: β=3.16, DBP: β=1.78, HTN: OR=1.34), and NH4_C18:2 CE had the strongest inverse association (SBP: β=-2.31, DBP: β=-1.49, HTN: OR=0.75), per SD increase of the metabolite level. The directions of associations were generally consistent across sex and racial/ethnic groups. Pathway enrichment analysis identified two enriched pathways (FDR&lt0.05): alanine, aspartate and glutamate metabolism and citrate cycle. Of 222 metabolites available for replication, 114 metabolites associated with all three traits (FDR&lt0.05) among 20,567 participants. MR analysis identified 18 potentially causal associations between metabolites and BP traits, including palmitoylcarnitine - a carnitine derivative that showed consistent positive causal associations across SBP, DBP and HTN (FDR&lt0.05).

A genetic association study of circulating coagulation factor VIII and von Willebrand factor levels

AbstractCoagulation factor VIII (FVIII) and its carrier protein von Willebrand factor (VWF) are critical to coagulation and platelet aggregation. We leveraged whole-genome sequence data from the Trans-Omics for Precision Medicine (TOPMed) program along with TOPMed-based imputation of genotypes in additional samples to identify genetic associations with circulating FVIII and VWF levels in a single-variant meta-analysis, including up to 45 289 participants. Gene-based aggregate tests were implemented in TOPMed. We identified 3 candidate causal genes and tested their functional effect on FVIII release from human liver endothelial cells (HLECs) and VWF release from human umbilical vein endothelial cells. Mendelian randomization was also performed to provide evidence for causal associations of FVIII and VWF with thrombotic outcomes. We identified associations (P < 5 × 10−9) at 7 new loci for FVIII (ST3GAL4, CLEC4M, B3GNT2, ASGR1, F12, KNG1, and TREM1/NCR2) and 1 for VWF (B3GNT2). VWF, ABO, and STAB2 were associated with FVIII and VWF in gene-based analyses. Multiphenotype analysis of FVIII and VWF identified another 3 new loci, including PDIA3. Silencing of B3GNT2 and the previously reported CD36 gene decreased release of FVIII by HLECs, whereas silencing of B3GNT2, CD36, and PDIA3 decreased release of VWF by HVECs. Mendelian randomization supports causal association of higher FVIII and VWF with increased risk of thrombotic outcomes. Seven new loci were identified for FVIII and 1 for VWF, with evidence supporting causal associations of FVIII and VWF with thrombotic outcomes. B3GNT2, CD36, and PDIA3 modulate the release of FVIII and/or VWF in vitro.

Transcriptome-wide association study of the plasma proteome reveals cis and trans regulatory mechanisms underlying complex traits

Regulation of transcription and translation are mechanisms through which genetic variants affect complex traits. Expression quantitative trait locus (eQTL) studies have been more successful at identifying cis-eQTL (within 1 Mb of the transcription start site) than trans-eQTL. Here, we tested the cis component of gene expression for association with observed plasma protein levels to identify cis- and trans-acting genes that regulate protein levels. We used transcriptome prediction models from 49 Genotype-Tissue Expression (GTEx) Project tissues to predict the cis component of gene expression and tested the predicted expression of every gene in every tissue for association with the observed abundance of 3,622 plasma proteins measured in 3,301 individuals from the INTERVAL study. We tested significant results for replication in 971 individuals from the Trans-omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA). We found 1,168 and 1,210 cis- and trans-acting associations that replicated in TOPMed (FDR<0.05) with a median expected true positive rate (π1) across tissues of 0.806 and 0.390, respectively. The target proteins of trans-acting genes were enriched for transcription factor binding sites and autoimmune diseases in the GWAS catalog. Furthermore, we found a higher correlation between predicted expression and protein levels of the same underlying gene (R= 0.17) than observed expression (R= 0.10, p= 7.50×10-11). This indicates the cis-acting genetically regulated (heritable) component of gene expression is more consistent across tissues than total observed expression (genetics+ environment) and is useful in uncovering the function of SNPs associated with complex traits.

Novel functional insights into ischemic stroke biology provided by the first genome-wide association study of stroke in indigenous Africans

BackgroundAfrican ancestry populations have the highest burden of stroke worldwide, yet the genetic basis of stroke in these populations is obscure. The Stroke Investigative Research and Educational Network (SIREN) is a multicenter study involving 16 sites in West Africa. We conducted the first-ever genome-wide association study (GWAS) of stroke in indigenous Africans.MethodsCases were consecutively recruited consenting adults (aged > 18 years) with neuroimaging-confirmed ischemic stroke. Stroke-free controls were ascertained using a locally validated Questionnaire for Verifying Stroke-Free Status. DNA genotyping with the H3Africa array was performed, and following initial quality control, GWAS datasets were imputed into the NIH Trans-Omics for Precision Medicine (TOPMed) release2 from BioData Catalyst. Furthermore, we performed fine-mapping, trans-ethnic meta-analysis, and in silico functional characterization to identify likely causal variants with a functional interpretation.ResultsWe observed genome-wide significant (P-value < 5.0E−8) SNPs associations near AADACL2 and miRNA (MIR5186) genes in chromosome 3 after adjusting for hypertension, diabetes, dyslipidemia, and cardiac status in the base model as covariates. SNPs near the miRNA (MIR4458) gene in chromosome 5 were also associated with stroke (P-value < 1.0E−6). The putative genes near AADACL2, MIR5186, and MIR4458 genes were protective and novel. SNPs associations with stroke in chromosome 2 were more than 77 kb from the closest gene LINC01854 and SNPs in chromosome 7 were more than 116 kb to the closest gene LINC01446 (P-value < 1.0E−6). In addition, we observed SNPs in genes STXBP5-AS1 (chromosome 6), GALTN9 (chromosome 12), FANCA (chromosome 16), and DLGAP1 (chromosome 18) (P-value < 1.0E−6). Both genomic regions near genes AADACL2 and MIR4458 remained significant following fine mapping.ConclusionsOur findings identify potential roles of regulatory miRNA, intergenic non-coding DNA, and intronic non-coding RNA in the biology of ischemic stroke. These findings reveal new molecular targets that promise to help close the current gaps in accurate African ancestry-based genetic stroke’s risk prediction and development of new targeted interventions to prevent or treat stroke.

Effect of genetic clusters related to insulin resistance on neurological traits in diverse populations from the Trans‐Omics for Precision Medicine Program

AbstractBackgroundInsulin resistance (IR) is a major risk factor for Alzheimer’s disease (AD) and is primarily driven by obesity, another AD risk factor. The biological mechanism by which IR predisposes to AD is unknown. Genetic clustering analyses of type 2 diabetes (T2D) loci can help characterize which mechanism of impaired insulin action may be involved in AD and related traits.MethodWe constructed five genetic clusters related to IR (Obesity, Lipodystrophy, Liver/Lipid, ALP [alkaline phosphatase] negative, and Hyper‐Insulin Secretion) based on a recent clustering analysis of T2D loci (PMID: 36538063). We evaluated the association of each cluster with the Homeostatic Model Assessment for Insulin Resistance (HOMA‐IR) and neurological traits (AD, dementia, general cognitive function, and four brain MRI volumes) in &gt;38k participants (36% men, mean age 55yrs (14.5)) of diverse race or ethnicity, from the Trans‐Omics for Precision Medicine (TOPMed) Program. We conducted pooled and race/ethnicity‐stratified association analyses (European‐50%, African‐American‐22.5%, and Hispanic/Latino‐21%). We used logistic or linear mixed‐effect models, adjusted for age, sex, study, and the first 11 genetic principal components. We accounted for relatedness and allowed for heterogeneous variances across studies.ResultWe confirmed the association of each IR genetic cluster with HOMA‐IR in the pooled analysis (2.6E‐66≤P≤2E‐4) and detected significant to suggestive associations in group‐stratified analyses (6.3E‐39≤P≤0.05), except for the Hyper‐Insulin Secretion cluster in African‐Americans (P = 0.30). The genetic clusters were not significantly associated with neurological outcomes after multiple testing adjustment (P = 0.05/Ngroups/Nclusters/Noutcomes = 0.05/4/5/7 = 3.6×10−4). We observed nominal associations (P&lt;0.05) for two genetic clusters. The Lipodystrophy cluster was negatively associated with intracranial volume in the pooled analysis (beta = ‐0.51, P = 0.003) as well as in European (beta = ‐0.47, P = 0.02) and Hispanic/Latino (beta = ‐1.23, P = 0.01) participants. The Hyper‐Insulin Secretion cluster was negatively associated with hippocampal volume in Hispanic/Latino participants (beta = ‐0.005, P = 0.03), and positively associated with AD and dementia (OR = 1.02 [1.001‐1.038], P = 0.03) in European participants.ConclusionOur findings are consistent with the literature suggesting that IR may be associated with lower brain volumes and increased AD risk. Our analyses shed light on two biological mechanisms of impaired insulin action potentially involved in AD and related traits, with genetic associations that may differ by race or ethnicity.Funding: R00AG066849

Multi-ancestry epigenome-wide analyses identify methylated sites associated with aortic augmentation index in TOPMed MESA

Despite the prognostic value of arterial stiffness (AS) and pulsatile hemodynamics (PH) for cardiovascular morbidity and mortality, epigenetic modifications that contribute to AS/PH remain unknown. To gain a better understanding of the link between epigenetics (DNA methylation) and AS/PH, we examined the relationship of eight measures of AS/PH with CpG sites and co-methylated regions using multi-ancestry participants from Trans-Omics for Precision Medicine (TOPMed) Multi-Ethnic Study of Atherosclerosis (MESA) with sample sizes ranging from 438 to 874. Epigenome-wide association analysis identified one genome-wide significant CpG (cg20711926-CYP1B1) associated with aortic augmentation index (AIx). Follow-up analyses, including gene set enrichment analysis, expression quantitative trait methylation analysis, and functional enrichment analysis on differentially methylated positions and regions, further prioritized three CpGs and their annotated genes (cg23800023-ETS1, cg08426368-TGFB3, and cg17350632-HLA-DPB1) for AIx. Among these, ETS1 and TGFB3 have been previously prioritized as candidate genes. Furthermore, both ETS1 and HLA-DPB1 have significant tissue correlations between Whole Blood and Aorta in GTEx, which suggests ETS1 and HLA-DPB1 could be potential biomarkers in understanding pathophysiology of AS/PH. Overall, our findings support the possible role of epigenetic regulation via DNA methylation of specific genes associated with AIx as well as identifying potential targets for regulation of AS/PH.

Rare variants in long non-coding RNAs are associated with blood lipid levels in the TOPMed whole-genome sequencing study

Long non-coding RNAs (lncRNAs) are known to perform important regulatory functions in lipid metabolism. Large-scale whole-genome sequencing (WGS) studies and new statistical methods for variant set tests now provide an opportunity to assess more associations between rare variants in lncRNA genes and complex traits across the genome. In this study, we used high-coverage WGS from 66,329 participants of diverse ancestries with measurement of blood lipids and lipoproteins (LDL-C, HDL-C, TC, and TG) in the National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) program to investigate the role of lncRNAs in lipid variability. We aggregated rare variants for 165,375 lncRNA genes based on their genomic locations and conducted rare-variant aggregate association tests using the STAAR (variant-set test for association using annotation information) framework. We performed STAAR conditional analysis adjusting for common variants in known lipid GWAS loci and rare-coding variants in nearby protein-coding genes. Our analyses revealed 83 rare lncRNA variant sets significantly associated with blood lipid levels, all of which were located in known lipid GWAS loci (in a±500-kb window of a Global Lipids Genetics Consortium index variant). Notably, 61 out of 83 signals (73%) were conditionally independent of common regulatory variation and rare protein-coding variation at the same loci. We replicated 34 out of 61 (56%) conditionally independent associations using the independent UK Biobank WGS data. Our results expand the genetic architecture of blood lipids to rare variants in lncRNAs.

Clonal Hematopoiesis of Indeterminate Potential (CHIP) and Incident Type 2 Diabetes Risk.

Clonal hematopoiesis of indeterminate potential (CHIP) is an aging-related accumulation of somatic mutations in hematopoietic stem cells, leading to clonal expansion. CHIP presence has been implicated in atherosclerotic coronary heart disease (CHD) and all-cause mortality, but its association with incident type 2 diabetes (T2D) is unknown. We hypothesized that CHIP is associated with elevated risk of T2D. CHIP was derived from whole-genome sequencing of blood DNA in the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine (TOPMed) prospective cohorts. We performed analysis for 17,637 participants from six cohorts, without prior T2D, cardiovascular disease, or cancer. We evaluated baseline CHIP versus no CHIP prevalence with incident T2D, including associations with DNMT3A, TET2, ASXL1, JAK2, and TP53 variants. We estimated multivariable-adjusted hazard ratios (HRs) and 95% CIs with adjustment for age, sex, BMI, smoking, alcohol, education, self-reported race/ethnicity, and combined cohorts' estimates via fixed-effects meta-analysis. Mean (SD) age was 63.4 (11.5) years, 76% were female, and CHIP prevalence was 6.0% (n = 1,055) at baseline. T2D was diagnosed in n = 2,467 over mean follow-up of 9.8 years. Participants with CHIP had 23% (CI 1.04, 1.45) higher risk of T2D than those with no CHIP. Specifically, higher risk was for TET2 (HR 1.48; CI 1.05, 2.08) and ASXL1 (HR 1.76; CI 1.03, 2.99) mutations; DNMT3A was nonsignificant (HR 1.15; CI 0.93, 1.43). Statistical power was limited for JAK2 and TP53 analyses. CHIP was associated with higher incidence of T2D. CHIP mutations located on genes implicated in CHD and mortality were also related to T2D, suggesting shared aging-related pathology.

A diverse ancestrally-matched reference panel increases genotype imputation accuracy in a underrepresented population

Variant imputation, a common practice in genome-wide association studies, relies on reference panels to infer unobserved genotypes. Multiple public reference panels are currently available with variations in size, sequencing depth, and represented populations. Currently, limited data exist regarding the performance of public reference panels when used in an imputation of populations underrepresented in the reference panel. Here, we compare the performance of various public reference panels: 1000 Genomes Project, Haplotype Reference Consortium, GenomeAsia 100 K, and the recent Trans-Omics for Precision Medicine (TOPMed) program, when used in an imputation of samples from the Thai population. Genotype yields were assessed, and imputation accuracies were examined by comparison with high-depth whole genome sequencing data of the same sample. We found that imputation using the TOPMed panel yielded the largest number of variants (~ 271 million). Despite being the smallest in size, GenomeAsia 100 K achieved the best imputation accuracy with a median genotype concordance rate of 0.97. For rare variants, GenomeAsia 100 K also offered the best accuracy, although rare variants were less accurately imputable than common variants (30.3% reduction in concordance rates). The high accuracy observed when using GenomeAsia 100 K is likely attributable to the diverse representation of populations genetically similar to the study cohort emphasizing the benefits of sequencing populations classically underrepresented in human genomics.

Predicted Proteome Association Studies of Breast, Prostate, Ovarian, and Endometrial Cancers Implicate Plasma Protein Regulation in Cancer Susceptibility.

Predicting protein levels from genotypes for proteome-wide association studies (PWAS) may provide insight into the mechanisms underlying cancer susceptibility. We performed PWAS of breast, endometrial, ovarian, and prostate cancers and their subtypes in several large European-ancestry discovery consortia (effective sample size: 237,483 cases/317,006 controls) and tested the results for replication in an independent European-ancestry GWAS (31,969 cases/410,350 controls). We performed PWAS using the cancer GWAS summary statistics and two sets of plasma protein prediction models, followed by colocalization analysis. Using Atherosclerosis Risk in Communities (ARIC) models, we identified 93 protein-cancer associations [false discovery rate (FDR) < 0.05]. We then performed a meta-analysis of the discovery and replication PWAS, resulting in 61 significant protein-cancer associations (FDR < 0.05). Ten of 15 protein-cancer pairs that could be tested using Trans-Omics for Precision Medicine (TOPMed) protein prediction models replicated with the same directions of effect in both cancer GWAS (P < 0.05). To further support our results, we applied Bayesian colocalization analysis and found colocalized SNPs for SERPINA3 protein levels and prostate cancer (posterior probability, PP = 0.65) and SNUPN protein levels and breast cancer (PP = 0.62). We used PWAS to identify potential biomarkers of hormone-related cancer risk. SNPs in SERPINA3 and SNUPN did not reach genome-wide significance for cancer in the original GWAS, highlighting the power of PWAS for novel locus discovery, with the added advantage of providing directions of protein effect. PWAS and colocalization are promising methods to identify potential molecular mechanisms underlying complex traits.

1468-P: Integrating Whole-Genome Sequence Analysis and eQTL Data from the TOPMed Program Improves Understanding of Type 2 Diabetes Risk Loci in Diverse Populations

Type 2 diabetes (T2D) has been well characterized by genetic studies with over 550 risk loci identified in mostly European populations. To better understand the genetic basis of T2D in diverse ancestries, we performed a whole genome sequence association analysis with T2D and related traits of fasting insulin (FI), glucose (FG) and hemoglobin A1c (HbA1c). We then used the colocalization method COLOC to evaluate if the risk locus and expression quantitative trait loci (eQTL) region shared a variant. Four relevant eQTL specific to tissues or cell types were obtained from the NHLBI Trans Omics for Precision Medicine (TOPMed) program, including 6,602 samples for whole blood, 1,265 for peripheral blood mononuclear cells (PBMC), 368 for T cells, and 352 for monocytes. A signal was considered colocalized if the posterior probability of colocalization (PP.coloc) was &amp;gt;0.5. Our analysis included 21,913 prevalent T2D cases, 61,036 controls, and up to 50,201 non-T2D individuals with FG, FI or HbA1c from African, Atlantic Islander, East Asian, European, Hispanic, and Samoan groups. Single variant analysis identified 35 variants in known regions that were significantly associated with T2D or related traits (P &amp;lt; 4.00 × 10−9). Among these regions, we detected six genes (SLC2A2, NKX6-3, KCNQ1, HBB, FN3K, and G6PD) having colocalizing signals in at least one relevant tissue or cell type. For example, variant rs1050828 near G6PD on chromosome X was strongly associated with HbA1c (P &amp;lt;1.0×10−200). Colocalization analysis revealed that rs1050828 also affected the gene expression in various tissues or cell types, including BCAP31 in whole blood, TKTL1 in PBMC, MPP1 in T cell, and RENBP in monocytes. This novel finding, obtained using TOPMed eQTL data from multiple cohorts, suggests a link between risk-associated variation and effector genes, providing insight into potential causal genes and regulatory variants at T2D risk loci in diverse populations. Disclosure N.Wang: None. D.A.Dicorpo: None. Y.Zhang: None. J.Wessel: None. A.Manning: None. Topmed diabetes working group: n/a. Funding National Institute of Diabetes and Digestive and Kidney Diseases (UM1DK078616, R01HL151855)

0031 Whole genome sequence analyses for Excessive Daytime Sleepiness in the NHLBI TOPMed Program

Abstract Introduction Excessive daytime sleepiness is associated with multiple sleep, cardiometabolic, and neuropsychiatric disorders. Previous genome-wide association studies (GWAS) of daytime sleepiness using a single question in European population revealed associations for multiple genes in central nervous pathways. However, findings are still limited and may not be generalizable to other populations. In this study, we performed the first whole-genome sequence (WGS) analyses for Epworth Sleepiness Scale (ESS) in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. Methods This study includes 15,201 individuals of diverse race and ethnic backgrounds from seven cohorts in the TOPMed. We performed GWAS of single common variants (MAF&amp;gt;0.01) and gene-based burden tests of low-frequency and rare functional variants (loss of function and missense; MAF&amp;lt; 0.05). We adjusted for age, sex, body mass index (BMI), study, race/ethnicity, and genetic relatedness using linear mixed effects models implemented in the GENESIS R package. Results This sample included 43.8% males with a mean age of 54.9 years and BMI 29.6kg/m2, of whom 21.3% were classified with excessive daytime sleepiness (ESS&amp;gt;=10). In single variant test, we identified one novel genomic locus at rs60249969 (on CELF2) significantly associated with ESS (p-value=3.91x10-8). Suggestive significant associations were observed at 5 novel loci that overlapped with PDE7A/DNAJC5B, AHI1/PDE7B, KCNH7, F5, and FBXL17 (p&amp;lt; 1x10-7). In gene-based burden test, we did not identify any gene significantly associated with the ESS after correcting for multiple comparisons (p&amp;lt; 2x10-6). Suggestive significant evidence was observed at OR1A2 (consisting of 41 missense variants; p-value=1.65x10-5), an olfactory receptor gene differentially expressed in choroid plexus in the brain implicated in neurodegenerative diseases. Conclusion In our investigation of common and rare variant associations with excessive daytime sleepiness as assessed by the ESS, we identified one significant and several suggestive novel loci that mapped to genes highly expressed in brain tissues, which may have implications on sleep wake control system. Our next step will include replication analyses using independent imputed samples. Support (if any) R01HL153814 (to H.W.), NHLBI R35HL135818 (to S.R.).

Performance and accuracy evaluation of reference panels for genotype imputation in sub-Saharan African populations

Based on evaluations of imputation performed on a genotype dataset consisting of about 11,000 sub-Saharan African (SSA) participants, we show Trans-Omics for Precision Medicine (TOPMed) and the African Genome Resource (AGR) to be currently the best panels for imputing SSA datasets. We report notable differences in the number of single-nucleotide polymorphisms (SNPs) that are imputed by different panels in datasets from East, West, and South Africa. Comparisons with a subset of 95 SSA high-coverage whole-genome sequences (WGSs) show that despite being about 20-fold smaller, the AGR imputed dataset has higher concordance with the WGSs. Moreover, the level of concordance between imputed and WGS datasets was strongly influenced by the extent of Khoe-San ancestry in a genome, highlighting the need for integration of not only geographically but also ancestrally diverse WGS data in reference panels for further improvement in imputation of SSA datasets. Approaches that integrate imputed data from different panels could also lead to better imputation.

The power of TOPMed imputation for the discovery of Latino-enriched rare variants associated with type 2 diabetes

Aims/hypothesisThe Latino population has been systematically underrepresented in large-scale genetic analyses, and previous studies have relied on the imputation of ungenotyped variants based on the 1000 Genomes (1000G) imputation panel, which results in suboptimal capture of low-frequency or Latino-enriched variants. The National Heart, Lung, and Blood Institute (NHLBI) Trans-Omics for Precision Medicine (TOPMed) released the largest multi-ancestry genotype reference panel representing a unique opportunity to analyse rare genetic variations in the Latino population. We hypothesise that a more comprehensive analysis of low/rare variation using the TOPMed panel would improve our knowledge of the genetics of type 2 diabetes in the Latino population.MethodsWe evaluated the TOPMed imputation performance using genotyping array and whole-exome sequence data in six Latino cohorts. To evaluate the ability of TOPMed imputation to increase the number of identified loci, we performed a Latino type 2 diabetes genome-wide association study (GWAS) meta-analysis in 8150 individuals with type 2 diabetes and 10,735 control individuals and replicated the results in six additional cohorts including whole-genome sequence data from the All of Us cohort.ResultsCompared with imputation with 1000G, the TOPMed panel improved the identification of rare and low-frequency variants. We identified 26 genome-wide significant signals including a novel variant (minor allele frequency 1.7%; OR 1.37, p=3.4 × 10−9). A Latino-tailored polygenic score constructed from our data and GWAS data from East Asian and European populations improved the prediction accuracy in a Latino target dataset, explaining up to 7.6% of the type 2 diabetes risk variance.Conclusions/interpretationOur results demonstrate the utility of TOPMed imputation for identifying low-frequency variants in understudied populations, leading to the discovery of novel disease associations and the improvement of polygenic scores.Data availabilityFull summary statistics are available through the Common Metabolic Diseases Knowledge Portal (https://t2d.hugeamp.org/downloads.html) and through the GWAS catalog (https://www.ebi.ac.uk/gwas/, accession ID: GCST90255648). Polygenic score (PS) weights for each ancestry are available via the PGS catalog (https://www.pgscatalog.org, publication ID: PGP000445, scores IDs: PGS003443, PGS003444 and PGS003445).Graphical abstract

5613165 INVESTIGATING VARIANTS ASSOCIATED WITH QUANTITATIVE RED BLOOD CELL PHENOTYPES FROM TOPMED PREDICTIONS IN AFRICAN SICKLE CELL DISEASE POPULATIONS.

Background: Sickle cell disease (SCD) is caused by a single point mutation (c.20A>T, p.Glu6Val), which results in the formation of mutant haemoglobin S (HbS) instead of haemoglobin A (HbA). It is a life-long inherited recessive disorder that predominantly affects people of African, Indian, and Mediterranean descent. The polymerisation of HbS within red blood cells (RBCs) is the primary pathophysiological alteration in SCD, which results in the sickle-shape of RBCs. Sickle-shaped RBCs are more prone to haemolysis and blocking blood flow, resulting in anaemia and painful episodes. Haematological indices including haematocrit (HCT), haemoglobin concentration (Hb), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH), mean corpuscular haemoglobin concentration (MCHC), red blood cell width (RDW) and RBC count indicate the size, development, and haemoglobin content of RBCs. Haematological indices are routinely measured as a means of monitoring SCD. A study recently found associations between 14 novel variants and haematological indices when using whole genome sequencing (WGS) data. The association between these variants and haematological indices have not yet been investigated in African SCD patients. This study aims to determine the associations between these variants and haematological indices in SCD patients from Cameroon and Senegal compared to non-SCD individuals. Aim: Investigating the association of 13 novel variants previously found in the Trans-Omics for Precision Medicine (TOPMed) project to haematological indices in African SCD patients. Methods: The 14 variants from the TOPMed study will be genotyped using a TaqMan Assay platform. Sanger sequencing will then be performed on 10% of the cohort to validate the TaqMan Assay result. The application R will be used for the descriptive statistics using sociodemographic, clinical, and haematological indices to evaluate the cases and controls. The Hardy-Weinberg Equilibrium will be used to ensure quality control. A linear regression analysis will be performed in R to determine the association between the variants and RBC phenotypes. Expected results: We are expecting to find an association between the SNPs and haematologocial indices. Conclusion: The variants of focus in this study were prioritized using WGS data, therefore capturing variants that are usually not included in GWAS arrays or imputation reference panels. The associations of the 13 novel variants with haematological indices have not yet been tested in African SCD populations. The results of this investigation could positively impact future studies into predictive modelling using haematological indices.

Genetic architecture of asthma in African American patients

Asthma is a chronic inflammatory disorder with a strong genetic inheritance. Although more than 100 loci were reported through the genome-wide association study of European populations, the genetic underpinning of asthma in African American individuals remains largely elusive. We aimed to identify genetic loci associated with asthma in African American individuals. Three cohorts were genotyped at the Children's Hospital of Philadelphia by using the Illumina single-nucleotide polymorphism array platform. Genotype imputation was performed by using the Trans-Omics for Precision Medicine (TOPMed) reference panel, which includes whole genome sequencing data from more than 100,000 individuals. Ameta-analysis of 3 Children's Hospital of Philadelphia cohorts and 10 Consortium on Asthma among African Ancestry Populations in the Americas cohorts, totaling 19,628 subjects, was conducted to identify genetic loci associated with asthma in African American individuals. Our study identified 12 loci surpassing the classical genome-wide significance threshold (5×10-8). Of those loci, 8 reached the stricter significance threshold (3×10-8). The 9p24.1 locus (rs10975467 [P= 1.63×10-8]) has previously been associated with asthma in European individuals. Six loci are associated with enhancer activities, 2 loci are in DNase I-hypersensitive regions, and all of them are associated with regulatory motifs. Moreover, the locus 11q13.4 (rs7480008) isanexpression quantitative trait locus of XRRA1 in lung(P= 9.4×10-10), and the locus 13q14.3 (rs1543525) is a splicing quantitative trait locus of DHRS12 in lung (P= 1.1×10-13). Our findings provide candidate genetic loci for therapeutic target identification and prioritization for African populations.

Genome-wide analysis of oxylipins and oxylipin profiles in a pediatric population.

Oxylipins are inflammatory biomarkers derived from omega-3 and-6 fatty acids implicated in inflammatory diseases but have not been studied in a genome-wide association study (GWAS). The aim of this study was to identify genetic loci associated with oxylipins and oxylipin profiles to identify biologic pathways and therapeutic targets for oxylipins. We conducted a GWAS of plasma oxylipins in 316 participants in the Diabetes Autoimmunity Study in the Young (DAISY). DNA samples were genotyped using the TEDDY-T1D Exome array, and additional variants were imputed using the Trans-Omics for Precision Medicine (TOPMed) multi-ancestry reference panel. Principal components analysis of 36 plasma oxylipins was used to capture oxylipin profiles. PC1 represented linoleic acid (LA)- and alpha-linolenic acid (ALA)-related oxylipins, and PC2 represented arachidonic acid (ARA)-related oxylipins. Oxylipin PC1, PC2, and the top five loading oxylipins from each PC were used as outcomes in the GWAS (genome-wide significance: p < 5×10-8). The SNP rs143070873 was associated with (p < 5×10-8) the LA-related oxylipin 9-HODE, and rs6444933 (downstream of CLDN11) was associated with the LA-related oxylipin 13 S-HODE. A locus between MIR1302-7 and LOC100131146, rs10118380 and an intronic variant in TRPM3 were associated with the ARA-related oxylipin 11-HETE. These loci are involved in inflammatory signaling cascades and interact with PLA2, an initial step to oxylipin biosynthesis. Genetic loci involved in inflammation and oxylipin metabolism are associated with oxylipin levels.