Whole-genome sequencing association analysis of quantitative red blood cell phenotypes: The NHLBI TOPMed program

A novel statistical framework for meta-analysis of total mediation effect with high-dimensional omics mediators in large-scale genomic consortia

Meta-analysis is used to aggregate the effects of interest across multiple studies, while its methodology is largely underexplored in mediation analysis, particularly in estimating the total mediation effect of high-dimensional omics mediators. Large-scale genomic consortia, such as the Trans-Omics for Precision Medicine (TOPMed) program, comprise multiple cohorts with diverse technologies to elucidate the genetic architecture and biological mechanisms underlying complex human traits and diseases. Leveraging the recent established asymptotic standard error of the R-squared (R2)-based mediation effect estimation for high-dimensional omics mediators, we have developed a novel meta-analysis framework requiring only summary statistics and allowing inter-study heterogeneity. Whereas the proposed meta-analysis can uniquely evaluate and account for potential effect heterogeneity across studies due to, for example, varying genomic profiling platforms, our extensive simulations showed that the developed method was more computationally efficient and yielded satisfactory operating characteristics comparable to analysis of the pooled individual-level data when there was no inter-study heterogeneity. We applied the developed method to 5 TOPMed studies with over 5800 participants to estimate the mediation effects of gene expression on age-related variation in systolic blood pressure and sex-related variation in high-density lipoprotein (HDL) cholesterol. The proposed method is available in R package MetaR2M on GitHub.

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.

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

ABSTRACT

Coagulation 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.

A novel statistical framework for meta-analysis of total mediation effect with high-dimensional omics mediators in large-scale genomic consortia

Meta-analysis is used to aggregate the effects of interest across multiple studies, while its methodology is largely underexplored in mediation analysis, particularly in estimating the total mediation effect of high-dimensional omics mediators. Large-scale genomic consortia, such as the Trans-Omics for Precision Medicine (TOPMed) program, comprise multiple cohorts with diverse technologies to elucidate the genetic architecture and biological mechanisms underlying complex human traits and diseases. Leveraging the recent established asymptotic standard error of the R-squared R 2 -based mediation effect estimation for high-dimensional omics mediators, we have developed a novel meta-analysis framework requiring only summary statistics and allowing inter-study heterogeneity. Whereas the proposed meta-analysis can uniquely evaluate and account for potential effect heterogeneity across studies due to, for example, varying genomic profiling platforms, our extensive simulations showed that the developed method was more computationally efficient and yielded satisfactory operating characteristics comparable to analysis of the pooled individual-level data when there was no inter-study heterogeneity. We applied the developed method to 8 TOPMed studies with over 5800 participants to estimate the mediation effects of gene expression on age-related variation in systolic blood pressure and sex-related variation in high-density lipoprotein (HDL) cholesterol. The proposed method is available in R package MetaR2M on GitHub.

Haemoglobin thresholds to define anaemia from age 6 months to 65 years: estimates from international data sources

BACKGROUND

Detection of anaemia is crucial for clinical medicine and public health. Current WHO anaemia definitions are based on statistical thresholds (fifth centiles) set more than 50 years ago. We sought to establish evidence for the statistical haemoglobin thresholds for anaemia that can be applied globally and inform WHO and clinical guidelines.

METHODS

In this analysis we identified international data sources from populations in the USA, England, Australia, China, the Netherlands, Canada, Ecuador, and Bangladesh with sufficient clinical and laboratory information collected between 1998 and 2020 to obtain a healthy reference sample. Individuals with clinical or biochemical evidence of a condition that could reduce haemoglobin concentrations were excluded. We estimated haemoglobin thresholds (ie, 5th centiles) for children aged 6-23 months, 24-59 months, 5-11 years, and 12-17 years, and adults aged 18-65 years (including during pregnancy) for individual datasets and pooled across data sources. We also collated findings from three large-scale genetic studies to summarise genetic variants affecting haemoglobin concentrations in different ancestral populations.

FINDINGS

We identified eight data sources comprising 18 individual datasets that were eligible for inclusion in the analysis. In pooled analyses, the haemoglobin fifth centile was 104·4 g/L (90% CI 103·5-105·3) in 924 children aged 6-23 months, 110·2 g/L (109·5-110·9) in 1874 children aged 24-59 months, and 114·4 g/L (113·6-115·2) in 1839 children aged 5-11 years. Values diverged by sex in adolescents and adults. In pooled analyses, the fifth centile was 122·2 g/L (90% CI 121·3-123·1) in 1741 female adolescents aged 12-17 years and 128·2 g/L (126·4-130·0) in 1103 male adolescents aged 12-17 years. In pooled analyses of adults aged 18-65 years, the fifth centile was 119·7 g/L (90% CI 119·1-120·3) in 3640 non-pregnant females and 134·9 g/L (134·2-135·6) in 2377 males. Fifth centiles in pregnancy were 110·3 g/L (90% CI 109·5-111·0) in the first trimester (n=772) and 105·9 g/L (104·0-107·7) in the second trimester (n=111), with insufficient data for analysis in the third trimester. There were insufficient data for adults older than 65 years. We did not identify ancestry-specific high prevalence of non-clinically relevant genetic variants that influence haemoglobin concentrations.

INTERPRETATION

Our results enable global harmonisation of clinical and public health haemoglobin thresholds for diagnosis of anaemia. Haemoglobin thresholds are similar between sexes until adolescence, after which males have higher thresholds than females. We did not find any evidence that thresholds should differ between people of differering ancestries.

FUNDING

World Health Organization and the Bill & Melinda Gates Foundation.

Improving polygenic risk prediction in admixed populations by explicitly modeling ancestral-differential effects via GAUDI

Polygenic risk scores (PRS) have shown successes in clinics, but most PRS methods focus only on participants with distinct primary continental ancestry without accommodating recently-admixed individuals with mosaic continental ancestry backgrounds for different segments of their genomes. Here, we develop GAUDI, a novel penalized-regression-based method specifically designed for admixed individuals. GAUDI explicitly models ancestry-differential effects while borrowing information across segments with shared ancestry in admixed genomes. We demonstrate marked advantages of GAUDI over other methods through comprehensive simulation and real data analyses for traits with associated variants exhibiting ancestral-differential effects. Leveraging data from the Women's Health Initiative study, we show that GAUDI improves PRS prediction of white blood cell count and C-reactive protein in African Americans by > 64% compared to alternative methods, and even outperforms PRS-CSx with large European GWAS for some scenarios. We believe GAUDI will be a valuable tool to mitigate disparities in PRS performance in admixed individuals.

The value of genetic data from 665,460 individuals in managing iron deficiency anaemia and suitability to donate blood

BACKGROUND AND OBJECTIVES

Although the genetic determinants of haemoglobin and ferritin have been widely studied, those of the clinically and globally relevant iron deficiency anaemia (IDA) and deferral due to hypohaemoglobinemia (Hb-deferral) are unclear. In this investigation, we aimed to quantify the value of genetic information in predicting IDA and Hb-deferral.

MATERIALS AND METHODS

We analysed genetic data from up to 665,460 participants of the FinnGen, Blood Service Biobank and UK Biobank, and used INTERVAL (N = 39,979) for validation. We performed genome-wide association studies (GWASs) of IDA and Hb-deferral and utilized publicly available genetic associations to compute polygenic scores for IDA, ferritin and Hb. We fitted models to estimate the effect sizes of these polygenic risk scores (PRSs) on IDA and Hb-deferral risk while accounting for the individual's age, sex, weight, height, smoking status and blood donation history.

RESULTS

Significant variants in GWASs of IDA and Hb-deferral appear to be a small subset of variants associated with ferritin and Hb. Effect sizes of genetic predictors of IDA and Hb-deferral are similar to those of age and weight which are typically used in blood donor management. A total genetic score for Hb-deferral was estimated for each individual. The odds ratio estimate between first decile against that at ninth decile of total genetic score distribution ranged from 1.4 to 2.2.

CONCLUSION

The value of genetic data in predicting IDA or suitability to donate blood appears to be on a practically useful level.

Mosaic chromosomal alterations in blood across ancestries using whole-genome sequencing

Megabase-scale mosaic chromosomal alterations (mCAs) in blood are prognostic markers for a host of human diseases. Here, to gain a better understanding of mCA rates in genetically diverse populations, we analyzed whole-genome sequencing data from 67,390 individuals from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine program. We observed higher sensitivity with whole-genome sequencing data, compared with array-based data, in uncovering mCAs at low mutant cell fractions and found that individuals of European ancestry have the highest rates of autosomal mCAs and the lowest rates of chromosome X mCAs, compared with individuals of African or Hispanic ancestry. Although further studies in diverse populations will be needed to replicate our findings, we report three loci associated with loss of chromosome X, associations between autosomal mCAs and rare variants in DCPS, ADM17, PPP1R16B and TET2 and ancestry-specific variants in ATM and MPL with mCAs in cis.

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. 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.

Toward Identification of Functional Sequences and Variants in Noncoding DNA

Understanding the noncoding part of the genome, which encodes gene regulation, is necessary to identify genetic mechanisms of disease and translate findings from genome-wide association studies into actionable results for treatments and personalized care. Here we provide an overview of the computational analysis of noncoding regions, starting from gene-regulatory mechanisms and their representation in data. Deep learning methods, when applied to these data, highlight important regulatory sequence elements and predict the functional effects of genetic variants. These and other algorithms are used to predict damaging sequence variants. Finally, we introduce rare-variant association tests that incorporate functional annotations and predictions in order to increase interpretability and statistical power.

Functional characterization of human genomic variation linked to polygenic diseases

The burden of human disease lies predominantly in polygenic diseases. Since the early 2000s, genome-wide association studies (GWAS) have identified genetic variants and loci associated with complex traits. These have ranged from variants in coding sequences to mutations in regulatory regions, such as promoters and enhancers, as well as mutations affecting mediators of mRNA stability and other downstream regulators, such as 5' and 3'-untranslated regions (UTRs), long noncoding RNA (lncRNA), and miRNA. Recent research advances in genetics have utilized a combination of computational techniques, high-throughput in vitro and in vivo screening modalities, and precise genome editing to impute the function of diverse classes of genetic variants identified through GWAS. In this review, we highlight the vastness of genomic variants associated with polygenic disease risk and address recent advances in how genetic tools can be used to functionally characterize them.

Whole-Genome Sequencing Analysis of Human Metabolome in Multi-Ethnic Populations

Circulating metabolite levels may reflect the state of the human organism in health and disease, however, the genetic architecture of metabolites is not fully understood. We have performed a whole-genome sequencing association analysis of both common and rare variants in up to 11,840 multi-ethnic participants from five studies with up to 1666 circulating metabolites. We have discovered 1985 novel variant-metabolite associations, and validated 761 locus-metabolite associations reported previously. Seventy-nine novel variant-metabolite associations have been replicated, including three genetic loci located on the X chromosome that have demonstrated its involvement in metabolic regulation. Gene-based analysis have provided further support for seven metabolite-replicated loci pairs and their biologically plausible genes. Among those novel replicated variant-metabolite pairs, follow-up analyses have revealed that 26 metabolites have colocalized with 21 tissues, seven metabolite-disease outcome associations have been putatively causal, and 7 metabolites might be regulated by plasma protein levels. Our results have depicted the genetic contribution to circulating metabolite levels, providing additional insights into understanding human disease.

Statistical haemoglobin thresholds to define anaemia across the lifecycle

Detection of anaemia is critical for clinical medicine and public health. Current WHO values that define anaemia are statistical thresholds (5 ^th centile) set over 50 years ago, and are presently <110g/L in children 6-59 months, <115g/L in children 5-11 years, <110g/L in pregnant women, <120g/L in children 12-14 years of age, <120g/L in non-pregnant women, and <130g/L in men. Haemoglobin is sensitive to iron and other nutrient deficiencies, medical illness and inflammation, and is impacted by genetic conditions; thus, careful exclusion of these conditions is crucial to obtain a healthy reference population. We identified data sources from which sufficient clinical and laboratory information was available to determine an apparently healthy reference sample. Individuals were excluded if they had any clinical or biochemical evidence of a condition that may diminish haemoglobin concentration. Discrete 5 ^th centiles were estimated along with two-sided 90% confidence intervals and estimates combined using a fixed-effect approach. Estimates for the 5 ^th centile of the healthy reference population in children were similar between sexes. Thresholds in children 6-23 months were 104.4g/L [90% CI 103.5, 105.3]; in children 24-59 months were 110.2g/L [109.5, 110.9]; and in children 5-11 years were 114.1g/L [113.2, 115.0]. Thresholds diverged by sex in adolescents and adults. In females and males 12-17 years, thresholds were 122.2g/L [121.3, 123.1] and 128.2 [126.4, 130.0], respectively. In adults 18-65 years, thresholds were 119.7g/L [119.1, 120.3] in non-pregnant females and 134.9g/L [134.2, 135.6] in males. Limited analyses indicated 5 ^th centiles in first-trimester pregnancy of 110.3g/L [109.5, 111.0] and 105.9g/L [104.0, 107.7] in the second trimester. All thresholds were robust to variations in definitions and analysis models. Using multiple datasets comprising Asian, African, and European ancestries, we did not identify novel high prevalence genetic variants that influence haemoglobin concentration, other than variants in genes known to cause important clinical disease, suggesting non-clinical genetic factors do not influence the 5 ^th centile between ancestries. Our results directly inform WHO guideline development and provide a platform for global harmonisation of laboratory, clinical and public health haemoglobin thresholds.

TMPRSS6 as a Therapeutic Target for Disorders of Erythropoiesis and Iron Homeostasis

TMPRSS6 is a serine protease highly expressed in the liver. Its role in iron regulation was first reported in 2008 when mutations in TMPRSS6 were shown to be the cause of iron-refractory iron deficiency anemia (IRIDA) in humans and in mouse models. TMPRSS6 functions as a negative regulator of the expression of the systemic iron-regulatory hormone hepcidin. Over the last decade and a half, growing understanding of TMPRSS6 biology and mechanism of action has enabled development of new therapeutic approaches for patients with diseases of erythropoiesis and iron homeostasis.ClinicalTrials.gov identifier NCT03165864.

AIMedGraph: a comprehensive multi-relational knowledge graph for precision medicine

The development of high-throughput molecular testing techniques has enabled the large-scale exploration of the underlying molecular causes of diseases and the development of targeted treatment for specific genetic alterations. However, knowledge to interpret the impact of genetic variants on disease or treatment is distributed in different databases, scientific literature studies and clinical guidelines. AIMedGraph was designed to comprehensively collect and interrogate standardized information about genes, genetic alterations and their therapeutic and diagnostic relevance and build a multi-relational, evidence-based knowledge graph. Graph database Neo4j was used to represent precision medicine knowledge as nodes and edges in AIMedGraph. Entities in the current release include 30 340 diseases/phenotypes, 26 140 genes, 187 541 genetic variants, 2821 drugs, 15 125 clinical trials and 797 911 supporting literature studies. Edges in this release cover 621 731 drug interactions, 9279 drug susceptibility impacts, 6330 pharmacogenomics effects, 30 339 variant pathogenicity and 1485 drug adverse reactions. The knowledge graph technique enables hidden knowledge inference and provides insight into potential disease or drug molecular mechanisms. Database URL: http://aimedgraph.tongshugene.net:8201.

Eligibility Considerations for Female Whole Blood Donors: Hemoglobin Levels and Iron Status in a Nationally Representative Population

Blood collection from minority populations improves the transfusion support of patients with sickle cell disease and thalassemia, but efforts are challenged by high deferral rates for hemoglobin (Hb) eligibility thresholds. This study sought to evaluate hemoglobin and iron status of a representative US female population to assess the suitability of 12.0 g/dL as minimum hemoglobin. Data were extracted from the National Health and Nutrition Examination Surveys (NHANES), 1999-2010. A national sample designed to reflect potential female blood donors (weight ≥110 lbs, not pregnant, no infectious marker reactivity, and no blood donation in past year) aged 16 to 49 years was analyzed for Hb and serum ferritin (SF) measures by race/ethnicity (N = 6937). Mean Hb and SF and the prevalence of iron deficiency ([ID] SF<12 ng/mL and SF<26 ng/mL) and low Hb (<12.5 g/dL and <12.0 g/dL) were estimated. Multivariable modified Poisson regression compared the prevalence for ID or low Hb at each cutoff by race/ethnicity. Mean SF values were higher and ID prevalence was lower in Non-Hispanic (NH) White (SF = 45.3 ng/mL, SF<12 ng/mL = 8.2%) than NH Black (SF = 39.6 ng/mL, SF<12 ng/mL = 14.2%) and Hispanic (SF = 36.5 ng/mL, SF<12 ng/mL = 12.7%) females. Compared to NH White females (13.7 g/dL), mean Hb was lower in NH Black (12.6 g/dL) and Hispanic females (13.4 g/dL). The percentage with Hb<12.5 g/dL was >4 times greater in NH Black (39.1%) and >2 times greater in Hispanic females (16.5%) compared to NH White (8.6%). Within 0.5 g/dL incremental categories of Hb, NH Black had higher mean SF levels and lower prevalence of SF<12 ng/mL or <26 ng/mL compared to NH White and Hispanic females. At Hb of 12.0 to 12.4g/dL, NH Black females had better measures of iron status (SF = 39.1 ng/mL, %SF<12 ng/mL = 12.0%) than NH White (SF = 33.6 ng/mL, %SF<12 ng/mL=15.8%) and Hispanic (SF = 30.4 ng/mL, %SF<12 ng/mL=15.5%) females whose Hb was 12.5 to 12.9 g/dL. Adjusting for age and Hb, the prevalence ratio for low SF was significantly lower in NH Black compared to NH White females at both SF<26 ng/mL (adjusted prevalence ratio [aPR] = 0.83, 95%CI = 0.76-0.92) and SF<12 ng/mL (aPR = 0.66, 95%CI = 0.52-0.83). NH Black females with Hb 12.0 to 12.4g/dL have better iron stores than NH White and Hispanic females whose Hb is 12.5 to 12.9 g/dL. The distribution of Hb and iron may support the safe collection of blood for female donors below the current Hb eligibility requirement of 12.5 g/dL.

Development of a nomogram model to predict malignant vasovagal syncope in Chinese children

Objective

Vasovagal syncope (VVS) is the commonest form of syncope, and malignant VVS draws substantial attention due to its life-threatening cardiac asystolic risk. This study aimed to explore the predictive role of a wide panel of clinical indicators for malignant VVS in children, and further to develop a nomogram model.

Methods

This is a retrospective case-control study. VVS is diagnosed based on head-up tilt test (HUTT). STATA software (version 14.0) was used for statistical analysis, and effect sizes are expressed as odds ratio (OR) and 95% confidence interval (CI).

Results

Total 370 children with VVS were analyzed, and of them 16 children had malignant VVS. Sixteen malignant VVS and 64 non-malignant VVS were matched on age and sex by a 1:4 propensity scores matching method. Mean corpuscular hemoglobin (MCH) and standard deviation of average RR intervals milliseconds (SDANN) were significantly and independently associated with malignant VVS after adjusting for confounders, with OR reaching 1.437 (95% CI: 1.044 to 1.979; P = 0.026) and 1.035 (95% CI: 1.003 to 1.068; P = 0.029), respectively. Calibration and discrimination analyses revealed that the addition of MCH and SDANN can enhance model performance. Then, a nomogram to predict malignant VVS was developed using general characteristics and two above significant factors, and higher values in medical history, number of syncope, MCH and SDANN were associated with a greater risk of malignant VVS.

Conclusion

MCH and SDANN were two promising factors for the development of malignant VVS, and modeling of significant factors in a nomogram can provide strong reference to aid clinical decision-making.

Whole genome sequences discriminate hereditary hemorrhagic telangiectasia phenotypes by non-HHT deleterious DNA variation

The abnormal vascular structures of hereditary hemorrhagic telangiectasia (HHT) often cause severe anemia due to recurrent hemorrhage, but HHT causal genes do not predict the severity of hematological complications. We tested for chance inheritance and clinical associations of rare deleterious variants in which loss-of-function causes bleeding or hemolytic disorders in the general population. In double-blinded analyses, all 104 patients with HHT from a single reference center recruited to the 100 000 Genomes Project were categorized on new MALO (more/as-expected/less/opposite) sub-phenotype severity scales, and whole genome sequencing data were tested for high impact variants in 75 HHT-independent genes encoding coagulation factors, or platelet, hemoglobin, erythrocyte enzyme, and erythrocyte membrane constituents. Rare variants (all gnomAD allele frequencies <0.003) were identified in 56 (75%) of these 75 HHT-unrelated genes. Deleteriousness assignments by Combined Annotation Dependent Depletion (CADD) scores >15 were supported by gene-level mutation significance cutoff scores. CADD >15 variants were identified in 38/104 (36.5%) patients with HHT, found for 1 in 10 patients within platelet genes; 1 in 8 within coagulation genes; and 1 in 4 within erythrocyte hemolytic genes. In blinded analyses, patients with greater hemorrhagic severity that had been attributed solely to HHT vessels had more CADD-deleterious variants in platelet (Spearman ρ = 0.25; P = .008) and coagulation (Spearman ρ = 0.21; P = .024) genes. However, the HHT cohort had 60% fewer deleterious variants in platelet and coagulation genes than expected (Mann-Whitney test P = .021). In conclusion, patients with HHT commonly have rare variants in genes of relevance to their phenotype, offering new therapeutic targets and opportunities for informed, personalized medicine strategies.

eSCAN: scan regulatory regions for aggregate association testing using whole-genome sequencing data

Multiple statistical methods for aggregate association testing have been developed for whole-genome sequencing (WGS) data. Many aggregate variants in a given genomic window and ignore existing knowledge to define test regions, resulting in many identified regions not clearly linked to genes, and thus, limiting biological understanding. Functional information from new technologies (such as Hi-C and its derivatives), which can help link enhancers to their effector genes, can be leveraged to predefine variant sets for aggregate testing in WGS data. Here, we propose the eSCAN (scan the enhancers) method for genome-wide assessment of enhancer regions in sequencing studies, combining the advantages of dynamic window selection in SCANG (SCAN the Genome), a previously developed method, with the advantages of incorporating putative regulatory regions from annotation. eSCAN, by searching in putative enhancers, increases statistical power and aids mechanistic interpretation, as demonstrated by extensive simulation studies. We also apply eSCAN for blood cell traits using NHLBI Trans-Omics for Precision Medicine WGS data. Results from real data analysis show that eSCAN is able to capture more significant signals, and these signals are of shorter length (indicating higher resolution fine-mapping capability) and drive association of larger regions detected by other methods.

From GWAS variant to function: A study of ∼148,000 variants for blood cell traits

Genome-wide association studies (GWASs) have identified hundreds of thousands of genetic variants associated with complex diseases and traits. However, most variants are noncoding and not clearly linked to genes, making it challenging to interpret these GWAS signals. We present a systematic variant-to-function study, prioritizing the most likely functional elements of the genome for experimental follow-up, for >148,000 variants identified for hematological traits. Specifically, we developed VAMPIRE: Variant Annotation Method Pointing to Interesting Regulatory Effects, an interactive web application implemented in R Shiny. This tool efficiently integrates and displays information from multiple complementary sources, including epigenomic signatures from blood-cell-relevant tissues or cells, functional and conservation summary scores, variant impact on protein and gene expression, chromatin conformation information, as well as publicly available GWAS and phenome-wide association study (PheWAS) results. Leveraging data generated from independently performed functional validation experiments, we demonstrate that our prioritized variants, genes, or variant-gene links are significantly more likely to be experimentally validated. This study not only has important implications for systematic and efficient revelation of functional mechanisms underlying GWAS variants for hematological traits but also provides a prototype that can be adapted to many other complex traits, paving the path for efficient variant-to-function (V2F) analyses.

REHE: Fast variance components estimation for linear mixed models

Linear mixed models are widely used in ecological and biological applications, especially in genetic studies. Reliable estimation of variance components is crucial for using linear mixed models. However, standard methods, such as the restricted maximum likelihood (REML), are computationally inefficient in large samples and may be unstable with small samples. Other commonly used methods, such as the Haseman-Elston (HE) regression, may yield negative estimates of variances. Utilizing regularized estimation strategies, we propose the restricted Haseman-Elston (REHE) regression and REHE with resampling (reREHE) estimators, along with an inference framework for REHE, as fast and robust alternatives that provide nonnegative estimates with comparable accuracy to REML. The merits of REHE are illustrated using real data and benchmark simulation studies.

Transcriptome-Wide Association Study of Blood Cell Traits in African Ancestry and Hispanic/Latino Populations

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.