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.

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.

Whole-exome sequencing study identifies four novel gene loci associated with diabetic kidney disease.

Diabetic kidney disease (DKD) is recognized as an important public health challenge. However, its genomic mechanisms are poorly understood. To identify rare variants for DKD, we conducted a whole-exome sequencing (WES) study leveraging large cohorts well-phenotyped for chronic kidney disease and diabetes. Our two-stage WES study included 4372 European and African ancestry participants from the Chronic Renal Insufficiency Cohort and Atherosclerosis Risk in Communities studies (stage 1) and 11 487 multi-ancestry Trans-Omics for Precision Medicine participants (stage 2). Generalized linear mixed models, which accounted for genetic relatedness and adjusted for age, sex and ancestry, were used to test associations between single variants and DKD. Gene-based aggregate rare variant analyses were conducted using an optimized sequence kernel association test implemented within our mixed model framework. We identified four novel exome-wide significant DKD-related loci through initiating diabetes. In single-variant analyses, participants carrying a rare, in-frame insertion in the DIS3L2 gene (rs141560952) exhibited a 193-fold increased odds [95% confidence interval (CI): 33.6, 1105] of DKD compared with noncarriers (P = 3.59 × 10-9). Likewise, each copy of a low-frequency KRT6B splice-site variant (rs425827) conferred a 5.31-fold higher odds (95% CI: 3.06, 9.21) of DKD (P = 2.72 × 10-9). Aggregate gene-based analyses further identified ERAP2 (P = 4.03 × 10-8) and NPEPPS (P = 1.51 × 10-7), which are both expressed in the kidney and implicated in renin-angiotensin-aldosterone system modulated immune response. In the largest WES study of DKD, we identified novel rare variant loci attaining exome-wide significance. These findings provide new insights into the molecular mechanisms underlying DKD.

A framework for detecting noncoding rare-variant associations of large-scale whole-genome sequencing studies.

Large-scale whole-genome sequencing studies have enabled analysis of noncoding rare-variant (RV) associations with complex human diseases and traits. Variant-set analysis is a powerful approach to study RV association. However, existing methods have limited ability in analyzing the noncoding genome. We propose a computationally efficient and robust noncoding RV association detection framework, STAARpipeline, to automatically annotate a whole-genome sequencing study and perform flexible noncoding RV association analysis, including gene-centric analysis and fixed window-based and dynamic window-based non-gene-centric analysis by incorporating variant functional annotations. In gene-centric analysis, STAARpipeline uses STAAR to group noncoding variants based on functional categories of genes and incorporate multiple functional annotations. In non-gene-centric analysis, STAARpipeline uses SCANG-STAAR to incorporate dynamic window sizes and multiple functional annotations. We apply STAARpipeline to identify noncoding RV sets associated with four lipid traits in 21,015 discovery samples from the Trans-Omics for Precision Medicine (TOPMed) program and replicate several of them in an additional 9,123 TOPMed samples. We also analyze five non-lipid TOPMed traits.

Age at Menopause, Leukocyte Telomere Length, and Coronary Artery Disease in Postmenopausal Women.

BACKGROUND: Premature menopause is a risk factor for accelerated cardiovascular aging, but underlying mechanisms remain incompletely understood. This study investigated the role of leukocyte telomere length (LTL), a marker of cellular aging and genomic instability, in the association of premature menopause with cardiovascular disease. METHODS: Participants from the UK Biobank and Women's Health Initiative with complete reproductive history and LTL measurements were included. Primary analyses tested the association between age at menopause and LTL using multivariable-adjusted linear regression. Secondary analyses stratified women by history of gynecologic surgery. Mendelian randomization was used to infer causal relationships between LTL and age at natural menopause. Multivariable-adjusted Cox regression and mediation analyses tested the joint associations of premature menopause and LTL with incident coronary artery disease. RESULTS: This study included 130 254 postmenopausal women (UK Biobank: n=122 224; Women's Health Initiative: n=8030), of whom 4809 (3.7%) had experienced menopause before age 40. Earlier menopause was associated with shorter LTL (meta-analyzed ß=-0.02 SD/5 years of earlier menopause [95% CI, -0.02 to -0.01]; P=7.2×10-12). This association was stronger and significant in both cohorts for women with natural/spontaneous menopause (meta-analyzed ß=-0.04 SD/5 years of earlier menopause [95% CI, -0.04 to -0.03]; P<2.2×10-16) and was independent of hormone therapy use. Mendelian randomization supported a causal association of shorter genetically predicted LTL with earlier age at natural menopause. LTL and age at menopause were independently associated with incident coronary artery disease, and mediation analyses indicated small but significant mediation effects of LTL in the association of menopausal age with coronary artery disease. CONCLUSIONS: Earlier age at menopause is associated with shorter LTL, especially among women with natural menopause. Accelerated telomere shortening may contribute to the heightened cardiovascular risk associated with premature menopause.

Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop.

Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes the major points and conclusions from this NIAID workshop. As a group, the investigators underscored the imperatives for rigorous analytic frameworks, integration of different omics data types, cross-disciplinary interaction, strategies for overcoming current limitations, and the overarching goal to improve scientific understanding and care of asthma and allergic diseases.

Recent progress in the genetic and epigenetic underpinnings of atopy.

In the past 2 years, there continue to be advances in our understanding of the genetic and epigenetic underpinnings of atopy pertaining to disease risk and disease severity. The joint role of genetics and the environment has been emphasized in multiple studies. Combining genetics with family history, biomarkers, and comorbidities is further refining our ability to predict the development of individual atopic diseases as well as the advancement of the atopic march. Polygenic risk scores will be an important next step for the field moving toward clinical translation of the genetic findings thus far. A systems biology approach, as illustrated by studies of the microbiome and epigenome, will be necessary to fully understand disease development and to develop increasingly targeted therapeutics.

Incorporating genetics in identifying peanut allergy risk and tailoring allergen immunotherapy: A perspective on the genetic findings from the LEAP trial.

Examining the genetics of peanut allergy (PA) in the context of clinical trial interventions and outcomes provides an opportunity to not only understand gene-environment interactions for PA risk but to also understand the benefit of allergen immunotherapy. A consistent theme in the genetics of food allergy is that in keeping with the dual allergen exposure hypothesis, barrier- and immune-related genes are most commonly implicated in food allergy and tolerance. With a focus on PA, we review how genetic risk factors across 3 genes (FLG, MALT1, and HLA-DQA1) have helped delineate distinct allergic characteristics and outcomes in the context of environmental interventions in the Learning Early about Peanut Allergy (LEAP) study and other clinical trials. We specifically consider and present a framework for genetic risk prediction for the development of PA and discuss how genetics, age, and oral consumption intertwine to predict PA outcome. Although there is some promise in this proposed framework, a better understanding of the mechanistic pathways by which PA develops and persists is needed to develop targeted therapeutics for established disease. Only by understanding the mechanisms by which PA develops, persists, and resolves can we identify adjuvants to oral immunotherapy to make older children and adults immunologically similar to their younger, more malleable counterparts and thus more likely to achieve long-term tolerance.

Epidermal differentiation complex genetic variation in atopic dermatitis and peanut allergy.

BACKGROUND: Deleterious variation in the epidermal differentiation complex (EDC) on chromosome 1 is a well-known genetic determinant of atopic dermatitis (AD) and has been associated with risk of peanut allergy (PA) in population-based studies. OBJECTIVE: Our aim was to determine the effect of genetic variation in the EDC on AD trajectory and risk of PA in early life. METHODS: Genome sequencing was used to measure genetic variation in the EDC in the Learning Early about Peanut Allergy (LEAP) study participants. Association tests were done to identify gene- and variant-level predicted deleterious variation associated with AD severity by using the Scoring Atopic Dermatitis (SCORAD) tool (n = 559) at baseline and each follow-up visit, as well as PA and food allergy in peanut avoiders (n = 275). Predicted deleterious variants included missense variants that were frameshift insertions, frameshift deletions, stop-gain mutations, or stop-loss mutations. Associations between variant load, SCORAD score, and PA were tested by using linear and generalized linear regression models. RESULTS: The genes FLG, FLG2, HRNR, and TCHH1 harbored the most predicted deleterious variation (30, 6, 3, and 1 variant, respectively). FLG variants were associated with SCORAD score at all time points; 4 variants (R1798X, R501X, S126X, and S761fs) drove the association with SCORAD score at each time point, and higher variant load was associated with greater AD severity over time. There was an association between these variants and PA, which remained significant independent of baseline AD severity (odds ratio = 2.63 [95% CI = 1.11-6.01] [P = .02]). CONCLUSIONS: Variation in FLG predicted to be deleterious is associated with AD severity at baseline and longitudinally and has an association with PA independent of baseline severity.

A functional genomics pipeline to identify high-value asthma and allergy CpGs in the human methylome.

BACKGROUND: DNA methylation of cytosines at cytosine-phosphate-guanine (CpG) dinucleotides (CpGs) is a widespread epigenetic mark, but genome-wide variation has been relatively unexplored due to the limited representation of variable CpGs on commercial high-throughput arrays. OBJECTIVES: To explore this hidden portion of the epigenome, this study combined whole-genome bisulfite sequencing with in silico evidence of gene regulatory regions to design a custom array of high-value CpGs. This study focused on airway epithelial cells from children with and without allergic asthma because these cells mediate the effects of inhaled microbes, pollution, and allergens on asthma and allergic disease risk. METHODS: This study identified differentially methylated regions from whole-genome bisulfite sequencing in nasal epithelial cell DNA from a total of 39 children with and without allergic asthma of both European and African ancestries. This study selected CpGs from differentially methylated regions, previous allergy or asthma epigenome-wide association studies (EWAS), or genome-wide association study loci, and overlapped them with functional annotations for inclusion on a custom Asthma&Allergy array. This study used both the custom and EPIC arrays to perform EWAS of allergic sensitization (AS) in nasal epithelial cell DNA from children in the URECA (Urban Environment and Childhood Asthma) birth cohort and using the custom array in the INSPIRE [Infant Susceptibility to Pulmonary Infections and Asthma Following RSV Exposure] birth cohort. Each CpG on the arrays was assigned to its nearest gene and its promotor capture Hi-C interacting gene and performed expression quantitative trait methylation (eQTM) studies for both sets of genes. RESULTS: Custom array CpGs were enriched for intermediate methylation levels compared to EPIC CpGs. Intermediate methylation CpGs were further enriched among those associated with AS and for eQTMs on both arrays. CONCLUSIONS: This study revealed signature features of high-value CpGs and evidence for epigenetic regulation of genes at AS EWAS loci that are robust to race/ethnicity, ascertainment, age, and geography.

The IL-4Rα Q576R polymorphism is associated with increased severity of atopic dermatitis and exaggerates allergic skin inflammation in mice.

BACKGROUND: Atopic dermatitis (AD) is characterized by TH2-dominated skin inflammation and systemic response to cutaneously encountered antigens. The TH2 cytokines IL-4 and IL-13 play a critical role in the pathogenesis of AD. The Q576->R576 polymorphism in the IL-4 receptor alpha (IL-4Rα) chain common to IL-4 and IL-13 receptors alters IL-4 signaling and is associated with asthma severity. OBJECTIVE: We sought to investigate whether the IL-4Rα R576 polymorphism is associated with AD severity and exaggerates allergic skin inflammation in mice. METHODS: Nighttime itching interfering with sleep, Rajka-Langeland, and Eczema Area and Severity Index scores were used to assess AD severity. Allergic skin inflammation following epicutaneous sensitization of mice 1 or 2 IL-4Rα R576 alleles (QR and RR) and IL-4Rα Q576 (QQ) controls was assessed by flow cytometric analysis of cells and quantitative RT-PCR analysis of cytokines in skin. RESULTS: The frequency of nighttime itching in 190 asthmatic inner-city children with AD, as well as Rajka-Langeland and Eczema Area and Severity Index scores in 1116 White patients with AD enrolled in the Atopic Dermatitis Research Network, was higher in subjects with the IL-4Rα R576 polymorphism compared with those without, with statistical significance for the Rajka-Langeland score. Following epicutaneous sensitization of mice with ovalbumin or house dust mite, skin infiltration by CD4+ cells and eosinophils, cutaneous expression of Il4 and Il13, transepidermal water loss, antigen-specific IgE antibody levels, and IL-13 secretion by antigen-stimulated splenocytes were significantly higher in RR and QR mice compared with QQ controls. Bone marrow radiation chimeras demonstrated that both hematopoietic cells and stromal cells contribute to the mutants' exaggerated allergic skin inflammation. CONCLUSIONS: The IL-4Rα R576 polymorphism predisposes to more severe AD and increases allergic skin inflammation in mice.

Secondary analyses for genome-wide association studies using expression quantitative trait loci.

Genome-wide association studies (GWAS) have successfully identified thousands of single nucleotide polymorphisms (SNPs) associated with complex traits; however, the identified SNPs account for a fraction of trait heritability, and identifying the functional elements through which genetic variants exert their effects remains a challenge. Recent evidence suggests that SNPs associated with complex traits are more likely to be expression quantitative trait loci (eQTL). Thus, incorporating eQTL information can potentially improve power to detect causal variants missed by traditional GWAS approaches. Using genomic, transcriptomic, and platelet phenotype data from the Genetic Study of Atherosclerosis Risk family-based study, we investigated the potential to detect novel genomic risk loci by incorporating information from eQTL in the relevant target tissues (i.e., platelets and megakaryocytes) using established statistical principles in a novel way. Permutation analyses were performed to obtain family-wise error rates for eQTL associations, substantially lowering the genome-wide significance threshold for SNP-phenotype associations. In addition to confirming the well known association between PEAR1 and platelet aggregation, our eQTL-focused approach identified a novel locus (rs1354034) and gene (ARHGEF3) not previously identified in a GWAS of platelet aggregation phenotypes. A colocalization analysis showed strong evidence for a functional role of this eQTL.

MUC5B, telomere length and longitudinal quantitative interstitial lung changes: the MESA Lung Study.

BACKGROUND: The MUC5B promoter variant (rs35705950) and telomere length are linked to pulmonary fibrosis and CT-based qualitative assessments of interstitial abnormalities, but their associations with longitudinal quantitative changes of the lung interstitium among community-dwelling adults are unknown. METHODS: We used data from participants in the Multi-Ethnic Study of Atherosclerosis with high-attenuation areas (HAAs, Examinations 1-6 (2000-2018)) and MUC5B genotype (n=4552) and telomere length (n=4488) assessments. HAA was defined as the per cent of imaged lung with attenuation of -600 to -250 Hounsfield units. We used linear mixed-effects models to examine associations of MUC5B risk allele (T) and telomere length with longitudinal changes in HAAs. Joint models were used to examine associations of longitudinal changes in HAAs with death and interstitial lung disease (ILD). RESULTS: The MUC5B risk allele (T) was associated with an absolute change in HAAs of 2.60% (95% CI 0.36% to 4.86%) per 10 years overall. This association was stronger among those with a telomere length below an age-adjusted percentile of 5% (p value for interaction=0.008). A 1% increase in HAAs per year was associated with 7% increase in mortality risk (rate ratio (RR)=1.07, 95% CI 1.02 to 1.12) for overall death and 34% increase in ILD (RR=1.34, 95% CI 1.20 to 1.50). Longer baseline telomere length was cross-sectionally associated with less HAAs from baseline scans, but not with longitudinal changes in HAAs. CONCLUSIONS: Longitudinal increases in HAAs were associated with the MUC5B risk allele and a higher risk of death and ILD.

Transcriptional profile of platelets and iPSC-derived megakaryocytes from whole-genome and RNA sequencing.

Genome-wide association studies have identified common variants associated with platelet-related phenotypes, but because these variants are largely intronic or intergenic, their link to platelet biology is unclear. In 290 normal subjects from the GeneSTAR Research Study (110 African Americans [AAs] and 180 European Americans [EAs]), we generated whole-genome sequence data from whole blood and RNA sequence data from extracted nonribosomal RNA from 185 induced pluripotent stem cell-derived megakaryocyte (MK) cell lines (platelet precursor cells) and 290 blood platelet samples from these subjects. Using eigenMT software to select the peak single-nucleotide polymorphism (SNP) for each expressed gene, and meta-analyzing the results of AAs and EAs, we identify (q-value < 0.05) 946 cis-expression quantitative trait loci (eQTLs) in derived MKs and 1830 cis-eQTLs in blood platelets. Among the 57 eQTLs shared between the 2 tissues, the estimated directions of effect are very consistent (98.2% concordance). A high proportion of detected cis-eQTLs (74.9% in MKs and 84.3% in platelets) are unique to MKs and platelets compared with peak-associated SNP-expressed gene pairs of 48 other tissue types that are reported in version V7 of the Genotype-Tissue Expression Project. The locations of our identified eQTLs are significantly enriched for overlap with several annotation tracks highlighting genomic regions with specific functionality in MKs, including MK-specific DNAse hotspots, H3K27-acetylation marks, H3K4-methylation marks, enhancers, and superenhancers. These results offer insights into the regulatory signature of MKs and platelets, with significant overlap in genes expressed, eQTLs detected, and enrichment within known superenhancers relevant to platelet biology.

De novo mutations across 1,465 diverse genomes reveal mutational insights and reductions in the Amish founder population.

De novo mutations (DNMs), or mutations that appear in an individual despite not being seen in their parents, are an important source of genetic variation whose impact is relevant to studies of human evolution, genetics, and disease. Utilizing high-coverage whole-genome sequencing data as part of the Trans-Omics for Precision Medicine (TOPMed) Program, we called 93,325 single-nucleotide DNMs across 1,465 trios from an array of diverse human populations, and used them to directly estimate and analyze DNM counts, rates, and spectra. We find a significant positive correlation between local recombination rate and local DNM rate, and that DNM rate explains a substantial portion (8.98 to 34.92%, depending on the model) of the genome-wide variation in population-level genetic variation from 41K unrelated TOPMed samples. Genome-wide heterozygosity does correlate with DNM rate, but only explains <1% of variation. While we are underpowered to see small differences, we do not find significant differences in DNM rate between individuals of European, African, and Latino ancestry, nor across ancestrally distinct segments within admixed individuals. However, we did find significantly fewer DNMs in Amish individuals, even when compared with other Europeans, and even after accounting for parental age and sequencing center. Specifically, we found significant reductions in the number of C→A and T→C mutations in the Amish, which seem to underpin their overall reduction in DNMs. Finally, we calculated near-zero estimates of narrow sense heritability (h2), which suggest that variation in DNM rate is significantly shaped by nonadditive genetic effects and the environment.

Lipid mediators are detectable in the nasal epithelium and differ by asthma status in female subjects.

BACKGROUND: Lipid mediators, bioactive products of polyunsaturated fatty acid metabolism, contribute to inflammation initiation and resolution in allergic diseases; however, their presence in lung-related biosamples has not been fully described. OBJECTIVE: We aimed to quantify lipid mediators in the nasal airway epithelium and characterize preliminary associations with asthma. METHODS: Using liquid chromatography-mass spectrometry, we conducted a pilot study to quantify 56 lipid mediators from nasal epithelial samples collected from 11 female participants of an outpatient asthma clinic and community controls (aged 30-55 years). We examined the presence of each compound using descriptive statistics to test whether lipid mediators could distinguish subjects with asthma (n = 8) from control subjects (n = 3) using linear regression and partial least squares discriminant analysis. RESULTS: Fifteen lipid mediators were detectable in all samples, including resolvin (Rv) D5 (RvD5), with the highest median concentrations (in pg/µg protein) of 13-HODE (126.481), 15-HETE (32.869), and 13-OxoODE (13.251). From linear regression adjusted for age, prostaglandin E2 (PGE2) had a trend (P < .1) for higher concentrations in patients with severe asthma compared to controls (mean difference, 0.95; 95% confidence interval, -0.04 to 1.95). Asthma patients had higher scores on principal component 3 compared to controls (mean difference, 2.42; 95% confidence interval, 0.89 to 3.96), which represented lower levels of proresolving 15-HEPE, 19,20-DiHDPA, RvD5, 14-HDHA, 17-HDHA, and 13-HOTrE. Most of these compounds were best at discriminating asthma cases from controls in partial least squares discriminant analysis. CONCLUSION: Lipid mediators are detectable in the nasal epithelium, and their levels distinguish asthma cases from controls.

Discovering metabolite quantitative trait loci in asthma using an isolated population.

BACKGROUND: Integration of metabolomics with genetics may advance understanding of disease pathogenesis but has been underused in asthma genetic studies. OBJECTIVE: We sought to discover new genetic effects in asthma and to characterize the molecular consequences of asthma genetic risk through integration with the metabolome in a homogeneous population. METHODS: From fasting serum samples collected on 348 Tangier Island residents, we quantified 2612 compounds using untargeted metabolomics. Genotyping was performed using Illumina's MEGA array imputed to the TOPMed reference panel. To prioritize metabolites for genome-wide association analysis, we performed a metabolome-wide association study with asthma, selecting asthma-associated metabolites with heritability q value less than 0.01 for genome-wide association analysis. We also tested the association between all metabolites and 8451 candidate asthma single nucleotide polymorphisms previously associated with asthma in the UK Biobank. We followed up significant associations by characterizing shared genetic signal for metabolites and asthma using colocalization analysis. For detailed Methods, please see this article's Online Repository at www.jacionline.org. RESULTS: A total of 60 metabolites were associated with asthma (P < .01), including 40 heritable metabolites tested in genome-wide association analysis. We observed a strong association peak for the endocannabinoid linoleoyl ethanolamide on chromosome 6 in VNN1 (P < 2.7 × 10-9). We found strong evidence (colocalization posterior probability >75%) for a shared causal variant between 3 metabolites and asthma, including the polyamine acisoga and variants in LPP, and derivative leukotriene B4 and intergenic variants in chr10p14. CONCLUSIONS: We identified novel metabolite quantitative trait loci with asthma associations. Identification and characterization of these genetically driven metabolites may provide insight into the functional consequences of genetic risk factors for asthma.

Polygenic prediction of atopic dermatitis improves with atopic training and filaggrin factors.

BACKGROUND: While numerous genetic loci associated with atopic dermatitis (AD) have been discovered, to date, work leveraging the combined burden of AD risk variants across the genome to predict disease risk has been limited. OBJECTIVES: This study aims to determine whether polygenic risk scores (PRSs) relying on genetic determinants for AD provide useful predictions for disease occurrence and severity. It also explicitly tests the value of including genome-wide association studies of related allergic phenotypes and known FLG loss-of-function (LOF) variants. METHODS: AD PRSs were constructed for 1619 European American individuals from the Atopic Dermatitis Research Network using an AD training dataset and an atopic training dataset including AD, childhood onset asthma, and general allergy. Additionally, whole genome sequencing data were used to explore genetic scoring specific to FLG LOF mutations. RESULTS: Genetic scores derived from the AD-only genome-wide association studies were predictive of AD cases (PRSAD: odds ratio [OR], 1.70; 95% CI, 1.49-1.93). Accuracy was first improved when PRSs were built off the larger atopy genome-wide association studies (PRSAD+: OR, 2.16; 95% CI, 1.89-2.47) and further improved when including FLG LOF mutations (PRSAD++: OR, 3.23; 95% CI, 2.57-4.07). Importantly, while all 3 PRSs correlated with AD severity, the best prediction was from PRSAD++, which distinguished individuals with severe AD from control subjects with OR of 3.86 (95% CI, 2.77-5.36). CONCLUSIONS: This study demonstrates how PRSs for AD that include genetic determinants across atopic phenotypes and FLG LOF variants may be a promising tool for identifying individuals at high risk for developing disease and specifically severe disease.

Efficient Variant Set Mixed Model Association Tests for Continuous and Binary Traits in Large-Scale Whole-Genome Sequencing Studies.

With advances in whole-genome sequencing (WGS) technology, more advanced statistical methods for testing genetic association with rare variants are being developed. Methods in which variants are grouped for analysis are also known as variant-set, gene-based, and aggregate unit tests. The burden test and sequence kernel association test (SKAT) are two widely used variant-set tests, which were originally developed for samples of unrelated individuals and later have been extended to family data with known pedigree structures. However, computationally efficient and powerful variant-set tests are needed to make analyses tractable in large-scale WGS studies with complex study samples. In this paper, we propose the variant-set mixed model association tests (SMMAT) for continuous and binary traits using the generalized linear mixed model framework. These tests can be applied to large-scale WGS studies involving samples with population structure and relatedness, such as in the National Heart, Lung, and Blood Institute's Trans-Omics for Precision Medicine (TOPMed) program. SMMATs share the same null model for different variant sets, and a virtue of this null model, which includes covariates only, is that it needs to be fit only once for all tests in each genome-wide analysis. Simulation studies show that all the proposed SMMATs correctly control type I error rates for both continuous and binary traits in the presence of population structure and relatedness. We also illustrate our tests in a real data example of analysis of plasma fibrinogen levels in the TOPMed program (n = 23,763), using the Analysis Commons, a cloud-based computing platform.

Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations.

Most genome-wide association and fine-mapping studies to date have been conducted in individuals of European descent, and genetic studies of populations of Hispanic/Latino and African ancestry are limited. In addition, these populations have more complex linkage disequilibrium structure. In order to better define the genetic architecture of these understudied populations, we leveraged >100,000 phased sequences available from deep-coverage whole genome sequencing through the multi-ethnic NHLBI Trans-Omics for Precision Medicine (TOPMed) program to impute genotypes into admixed African and Hispanic/Latino samples with genome-wide genotyping array data. We demonstrated that using TOPMed sequencing data as the imputation reference panel improves genotype imputation quality in these populations, which subsequently enhanced gene-mapping power for complex traits. For rare variants with minor allele frequency (MAF) < 0.5%, we observed a 2.3- to 6.1-fold increase in the number of well-imputed variants, with 11-34% improvement in average imputation quality, compared to the state-of-the-art 1000 Genomes Project Phase 3 and Haplotype Reference Consortium reference panels. Impressively, even for extremely rare variants with minor allele count <10 (including singletons) in the imputation target samples, average information content rescued was >86%. Subsequent association analyses of TOPMed reference panel-imputed genotype data with hematological traits (hemoglobin (HGB), hematocrit (HCT), and white blood cell count (WBC)) in ~21,600 African-ancestry and ~21,700 Hispanic/Latino individuals identified associations with two rare variants in the HBB gene (rs33930165 with higher WBC [p = 8.8x10-15] in African populations, rs11549407 with lower HGB [p = 1.5x10-12] and HCT [p = 8.8x10-10] in Hispanics/Latinos). By comparison, neither variant would have been genome-wide significant if either 1000 Genomes Project Phase 3 or Haplotype Reference Consortium reference panels had been used for imputation. Our findings highlight the utility of the TOPMed imputation reference panel for identification of novel rare variant associations not previously detected in similarly sized genome-wide studies of under-represented African and Hispanic/Latino populations.