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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.Our aim was to determine the effect of genetic variation in the EDC on AD trajectory and risk of PA in early life.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.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]).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.

Since the publication of the first draft of the human genome, there has been an explosion of new technologies with increasing power to interrogate the totality of biological molecules (e.g. DNA, RNA, proteins, metabolites) and their modifications (e.g. DNA methylation, histone modifications). These technologies, collectively called omics, have been widely applied in the last two decades to study biological systems to gain deeper insight into mechanisms driving the physiology and pathophysiology of human health and disease. Due to its complex, multifactorial nature, food allergy is especially well-suited to be investigated using omics approaches. In this rostrum, we review how omic technologies have been applied to explore diverse aspects of food allergy, including adaptive and innate immune processes in food allergic responses, the role of the microbiome in food allergy risk, metabolic changes in the gut and blood associated with food allergy, as well as the identification of biomarkers and potential therapeutic targets for the condition. We discuss the strengths and limitations of the studies performed thus far and the need to adopt systems biology approaches that integrate data from multiple omics to fully leverage the potential of these technologies to advance food allergy research and care.

Human genetic studies support an inverse causal relationship between leukocyte telomere length (LTL) and coronary artery disease (CAD), but directionally mixed effects for LTL and diverse malignancies. Clonal hematopoiesis of indeterminate potential (CHIP), characterized by expansion of hematopoietic cells bearing leukemogenic mutations, predisposes both hematologic malignancy and CAD. TERT (which encodes telomerase reverse transcriptase) is the most significantly associated germline locus for CHIP in genome-wide association studies. Here, we investigated the relationship between CHIP, LTL, and CAD in the Trans-Omics for Precision Medicine (TOPMed) program ( n = 63,302) and UK Biobank ( n = 47,080). Bidirectional Mendelian randomization studies were consistent with longer genetically imputed LTL increasing propensity to develop CHIP, but CHIP then, in turn, hastens to shorten measured LTL (mLTL). We also demonstrated evidence of modest mediation between CHIP and CAD by mLTL. Our data promote an understanding of potential causal relationships across CHIP and LTL toward prevention of CAD.

Humans have undergone intense evolutionary selection to optimize their capacity to generate necessary quantities of long chain (LC-) polyunsaturated fatty acid (PUFA)-containing lipids. To better understand the impact of genetic variation within a locus of three FADS genes (FADS1, FADS2, and FADS3) on a diverse family of lipids, we examined the associations of 247 lipid metabolites (including four major classes of LC-PUFA-containing molecules and signaling molecules) with common and low-frequency genetic variants located within the FADS locus. Genetic variation in the FADS locus was strongly associated (p –8) with 52 LC-PUFA-containing lipids and signaling molecules, including free fatty acids, phospholipids, lyso-phospholipids, and an endocannabinoid. Notably, the majority (80%) of FADS-associated lipids were not significantly associated with genetic variants outside of this FADS locus. These findings highlight the central role genetic variation at the FADS locus plays in regulating levels of physiologically critical LC-PUFA-containing lipids that participate in innate immunity, energy homeostasis, and brain development/function.

Background Unexplained heterogeneity in clinical trials has resulted in questions regarding the effectiveness of ɣ-linolenic acid (GLA)-containing botanical oil supplements. This heterogeneity may be explained by genetic variation within the fatty acid desaturase (FADS) gene cluster that is associated with circulating and tissue concentrations of arachidonic acid (ARA) and dihomo-ɣ-linolenic acid (DGLA), both of which may be synthesized from GLA and result in proinflammatory and anti-inflammatory metabolites, respectively. Objectives The objective of this study was to prospectively compare the capacity of a non-Hispanic white cohort, stratified by FADS genotype at the key single-nucleotide polymorphism (SNP) rs174537, to metabolize 18-carbon omega-6 (n-6) PUFAs in borage oil (BO) and soybean oil (SO) to GLA, DGLA, and ARA. Methods Healthy adults (n = 64) participated in a randomized, double-blind, crossover intervention. Individuals received encapsulated BO (Borago officinalis L.; 37% LA and 23% GLA) or SO [Glycine max (L.) Merr.; 50% LA and 0% GLA] for 4 wk, followed by an 8-wk washout period, before consuming the opposite oil for 4 wk. Serum lipids and markers of inflammation (C-reactive protein) were assessed for both oil types at baseline and during weeks 2 and 4 of the intervention. Results SO supplementation failed to alter circulating concentrations of any n-6 long-chain PUFAs. In contrast, a modest daily dose of BO elevated serum concentrations of GLA and DGLA in an rs174537 genotype-dependent manner. In particular, DGLA increased by 57% (95% CI: 0.38, 0.79) in GG genotype individuals, but by 141% (95% CI: 1.03, 2.85) in TT individuals. For ARA, baseline concentrations varied substantially by genotype and increased modestly with BO supplementation, suggesting a key role for FADS variation in the balance of DGLA and ARA. Conclusions The results of this study clearly suggest that personalized and population-based approaches considering FADS genetic variation may be necessary to optimize the design of future clinical studies with GLA-containing oils. This trial was registered at clinicaltrials.gov as NCT02337231.

In recent years, the genomics community has witnessed the growth of large research biobanks, which collect DNA samples for research purposes. Depending on how and where the samples are genotyped, biobanks also offer the potential opportunity to return actionable genomic results to the clinical setting. We developed a preemptive clinical pharmacogenomic implementation initiative via a health system-wide research biobank at the University of Colorado. Here, we describe how preemptive return of clinical pharmacogenomic results via a research biobank is feasible, particularly when coupled with strong institutional support to maximize the impact and efficiency of biobank resources, a multidisciplinary implementation team, automated clinical decision support tools, and proactive strategies to engage stakeholders early in the clinical decision support tool development process.

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency < 1%) predicted damaging coding variation by using sequence data from >170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels; some of these genes have not been previously associated with lipid levels when using rare coding variation from population-based samples. We prioritize 32 genes in array-based genome-wide association study (GWAS) loci based on aggregations of rare coding variants; three (EVI5, SH2B3, and PLIN1) had no prior association of rare coding variants with lipid levels. Most of our associated genes showed evidence of association among multiple ancestries. Finally, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes and for genes closest to GWAS index single-nucleotide polymorphisms (SNPs). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels. This work was supported by a grant from the Swedish Research Council (2016-06830) and grants from the National Heart, Lung, and Blood Institute (NHLBI): R01HL142711 and R01HL127564. Please refer to the supplemental information for the full acknowledgements. Sí

BACKGROUND: Integration of metabolomics with genetics may advance understanding of disease pathogenesis but has been underutilized in asthma genetic studies. OBJECTIVE: We aimed to discover new genetic effects in asthma and to characterize the molecular consequences of asthma genetic risk through integration with the metabolome in a homogenous population. METHODS: From fasting serum samples collected on 348 Tangier Island residents, we quantified 2,612 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 (GWAS), we performed a metabolome-wide association study with asthma, selecting asthma-associated metabolites with heritability q-value75%) for a shared causal variant between three metabolites and asthma, including the polyamine acisoga and variants in LPP, and derivative leukotriene B4 and intergenic variants in chr10p14. CONCLUSION: 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.

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.We aimed to quantify lipid mediators in the nasal airway epithelium and characterize preliminary associations with asthma.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.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 ELipid mediators are detectable in the nasal epithelium, and their levels distinguish asthma cases from controls.

Human diets in developed countries such as the US have changed dramatically over the past 75 years, leading to increased obesity, inflammation, and cardiometabolic dysfunction. Evidence over the past decade indicates that the interaction of genetic variation with changes in the intake of 18-carbon essential dietary omega-6 (n-6) and omega-3 (n-3) polyunsaturated fatty acids (PUFA), linoleic acid (LA) and α-linolenic acid (ALA), respectively, has impacted numerous molecular and clinical phenotypes. Interactions are particularly relevant with the

Plasma lipids are heritable modifiable causal factors for coronary artery disease, the leading cause of death globally. Despite the well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing, partly due to limited sample sizes, ancestral diversity, and interpretation of potential clinical significance. Increasingly larger whole genome sequence datasets with plasma lipids coupled with methodologic advances enable us to more fully catalog the allelic spectrum for lipids. Here, among 66,329 ancestrally diverse (56% non-European ancestry) participants, we associate 428M variants from deep-coverage whole genome sequences with plasma lipids. Approximately 400M of these variants were not studied in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with plasma lipids through analysis of common and rare coding variants. We additionally discover several significantly associated rare non-coding variants largely at Mendelian lipid genes. Notably, we detect rareLDLRintronic variants associated with markedly increased LDL-C, similar to rareLDLRexonic variants. In conclusion, we conducted a systematic whole genome scan for plasma lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

Blood lipids are heritable modifiable causal factors for coronary artery disease. Despite well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing (WGS), partly due to limited sample sizes, ancestral diversity, and interpretation of clinical significance. Among 66,329 ancestrally diverse (56% non-European) participants, we associate 428M variants from deep-coverage WGS with lipid levels; ~400M variants were not assessed in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with blood lipids through analysis of common and rare coding variants. We discover several associated rare non-coding variants, largely at Mendelian lipid genes. Notably, we observe rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for blood lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

Long chain polyunsaturated fatty acids (LC-PUFAs) have critical signaling roles that regulate dyslipidemia and inflammation. Genetic variation in the FADS gene cluster accounts for a large portion of interindividual differences in circulating and tissue levels of LC-PUFAs, with the genotypes most strongly predictive of low LC-PUFA levels at strikingly higher frequencies in Amerind ancestry populations. In this study, we examined relationships between genetic ancestry and FADS variation in 1102 Hispanic American participants from the Multi-Ethnic Study of Atherosclerosis. We demonstrate strong negative associations between Amerind genetic ancestry and LC-PUFA levels. The FADS rs174537 single nucleotide polymorphism (SNP) accounted for much of the AI ancestry effect on LC-PUFAs, especially for low levels of n-3 LC-PUFAs. Rs174537 was also strongly associated with several metabolic, inflammatory and anthropomorphic traits including circulating triglycerides (TGs) and E-selectin in MESA Hispanics. Our study demonstrates that Amerind ancestry provides a useful and readily available tool to identify individuals most likely to have FADS-related n-3 LC-PUFA deficiencies and associated cardiovascular risk., Yang et al. examine whether genetic ancestry is associated with genetic variation in fatty acid desaturases and plasma phospholipid levels of long chain polyunsaturated fatty acids (LC-PUFAs) in Hispanic Americans. They find strong associations between Amerind genetic ancestry and LC-PUFA levels; and report that the well-known FADS rs174537 variant was associated with several metabolic, inflammatory and anthropomorphic traits including circulating triglycerides.

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). 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.

Analyses of data from genome-wide association studies on unrelated individuals have shown that, for human traits and diseases, approximately one-third to two-thirds of heritability is captured by common SNPs. However, it is not known whether the remaining heritability is due to the imperfect tagging of causal variants by common SNPs, in particular whether the causal variants are rare, or whether it is overestimated due to bias in inference from pedigree data. Here we estimated heritability for height and body mass index (BMI) from whole-genome sequence data on 25,465 unrelated individuals of European ancestry. The estimated heritability was 0.68 (standard error 0.10) for height and 0.30 (standard error 0.10) for body mass index. Low minor allele frequency variants in low linkage disequilibrium (LD) with neighboring variants were enriched for heritability, to a greater extent for protein-altering variants, consistent with negative selection. Our results imply that rare variants, in particular those in regions of low linkage disequilibrium, are a major source of the still missing heritability of complex traits and disease.

BackgroundAsthma is a common but complex disease with racial/ethnic differences in prevalence, morbidity, and response to therapies.ObjectiveWe sought to perform an analysis of genetic ancestry to identify new loci that contribute to asthma susceptibility.MethodsWe leveraged the mixed ancestry of 3902 Latinos and performed an admixture mapping meta-analysis for asthma susceptibility. We replicated associations in an independent study of 3774 Latinos, performed targeted sequencing for fine mapping, and tested for disease correlations with gene expression in the whole blood of more than 500 subjects from 3 racial/ethnic groups.ResultsWe identified a genome-wide significant admixture mapping peak at 18q21 in Latinos (P=6.8×10-6), where Native American ancestry was associated with increased risk of asthma (odds ratio [OR], 1.20; 95% CI, 1.07-1.34; P=.002) and European ancestry was associated with protection (OR, 0.86; 95% CI, 0.77-0.96; P=.008). Our findings were replicated in an independent childhood asthma study in Latinos (P=5.3×10-3, combined P=2.6×10-7). Fine mapping of 18q21 in 1978 Latinos identified a significant association with multiple variants 5' of SMAD family member 2 (SMAD2) in Mexicans, whereas a single rare variant in the same window was the top association in Puerto Ricans. Low versus high SMAD2 blood expression was correlated with case status (13.4% lower expression; OR, 3.93; 95% CI, 2.12-7.28; P&nbsp

High-throughput, "next-generation" sequencing methods are now being broadly applied across all fields of biomedical research, including respiratory disease, critical care, and sleep medicine. Although there are numerous review articles and best practice guidelines related to sequencing methods and data analysis, there are fewer resources summarizing issues related to study design and interpretation, especially as applied to common, complex, nonmalignant diseases. To address these gaps, a single-day workshop was held at the American Thoracic Society meeting in May 2017, led by the American Thoracic Society Section on Genetics and Genomics. The aim of this workshop was to review the design, analysis, interpretation, and functional follow-up of high-throughput sequencing studies in respiratory, critical care, and sleep medicine research. This workshop brought together experts in multiple fields, including genetic epidemiology, biobanking, bioinformatics, and research ethics, along with physician-scientists with expertise in a range of relevant diseases. The workshop focused on application of DNA and RNA sequencing research in common chronic diseases and did not cover sequencing studies in lung cancer, monogenic diseases (e.g., cystic fibrosis), or microbiome sequencing. Participants reviewed and discussed study design, data analysis and presentation, interpretation, functional follow-up, and reporting of results. This report summarizes the main conclusions of the workshop, specifically addressing the application of these methods in respiratory, critical care, and sleep medicine research. This workshop report may serve as a resource for our research community as well as for journal editors and reviewers of sequencing-based manuscript submissions in our research field.

BackgroundUse of targeted exome-arrays with common, rare variants and functionally enriched variation has led to discovery of new genes contributing to population variation in risk factors. Plasminogen activator-inhibitor 1 (PAI-1), tissue plasminogen activator (tPA), and the plasma product D-dimer are important components of the fibrinolytic system. There have been few large-scale genome-wide or exome-wide studies of PAI-1, tPA, and D-dimer.ObjectivesWe sought to discover new genetic loci contributing to variation in these traits using an exome-array approach.MethodsCohort-level analyses and fixed effects meta-analyses of PAI-1 (n=15603), tPA (n=6876,) and D-dimer (n=19306) from 12 cohorts of European ancestry with diverse study design were conducted, including single-variant analyses and gene-based burden testing.ResultsFive variants located in NME7, FGL1, and the fibrinogen locus, all associated with D-dimer levels, achieved genome-wide significance (P&nbsp

Since the onset of the SARS-CoV-2 pandemic, most clinical testing has focused on RT-PCR1. Host epigenome manipulation post coronavirus infection2–4 suggests that DNA methylation signatures may differentiate patients with SARS-CoV-2 infection from uninfected individuals, and help predict COVID-19 disease severity, even at initial presentation. We customized Illumina’s Infinium MethylationEPIC array to enhance immune response detection and profiled peripheral blood samples from 164 COVID-19 patients with longitudinal measurements of disease severity and 296 patient controls. Epigenome-wide association analysis revealed 13,033 genome-wide significant methylation sites for case-vs-control status. Genes and pathways involved in interferon signaling and viral response were significantly enriched among differentially methylated sites. We observe highly significant associations at genes previously reported in genetic association studies (e.g. IRF7, OAS1). Using machine learning techniques, models built using sparse regression yielded highly predictive findings: cross-validated best fit AUC was 93.6% for case-vs-control status, and 79.1%, 80.8%, and 84.4% for hospitalization, ICU admission, and progression to death, respectively. In summary, the strong COVID-19-specific epigenetic signature in peripheral blood driven by key immune-related pathways related to infection status, disease severity, and clinical deterioration provides insights useful for diagnosis and prognosis of patients with viral infections. Viral infections affect the body in many ways, including via changes to the epigenome, the sum of chemical modifications to an individual’s collection of genes that affect gene activity. Here, we analyzed the epigenome in blood samples from people with and without COVID-19 to determine whether we could find changes consistent with SARS-CoV-2 infection. Using a combination of statistical and machine learning techniques, we identify markers of SARS-CoV-2 infection as well as of severity and progression of COVID-19 disease. These signals of disease progression were present from the initial blood draw when first walking into the hospital. Together, these approaches demonstrate the potential of measuring the epigenome for monitoring SARS-CoV-2 status and severity. Konigsberg et al. profile DNA methylation in blood samples from SARS-CoV-2 cases and controls. The authors use machine learning to classify infected vs. non-infected individuals and predict clinical outcomes related to disease severity.

Traditional Hardy–Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in data sets composed of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and to evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence data sets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false-positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently among the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

The genetic determinants of fasting glucose (FG) and fasting insulin (FI) have been studied mostly through genome and exome arrays, resulting in over 100 associated variants. We extended this work with a high-coverage whole genome sequencing (WGS) analysis from fifteen cohorts in the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. More than 23,000 non-diabetic individuals from five self-reported race/ethnicities (African, Asian, European, Hispanic and Samoan) were included for each trait. We analyzed 60M variants in race/ethnicity-specific and pooled single variant and rare variant aggregate tests. Twenty-two variants across sixteen gene regions were found significantly associated with FG or FI, eight of which were rare (Minor Allele Frequency, MAFG6PC2locus we identified a distinct FG signal at rare variant rs2232326 (MAF=0.01) after conditioning on known common variants. Functional annotations show rs2232326 to be disruptive and likely damaging while being weakly transcribed in islets. A pair of FG-associated variants were identified near theSLC30A8locus. These variants, one of which was rare (MAF=0.001) and Asian race/ethnicity-specific, were shown to be in islet-specific active enhancer regions. Other associated regions include rare variants nearROBO1andPTPRT, and common variants nearMTNR1B, GCK, GCKR, FOXA2, APOB, TCF7L2, andADCY5. We provide a catalog of nucleotide-resolution genomic variation spanning intergenic and intronic regions down to a minor allele count of 20, creating a foundation for future sequencing-based investigation of glycemic traits.

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase statistical power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data-set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data-sharing mechanisms. This system was developed for the National Heart, Lung, and Blood Institute’s Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other -omics data for more than 80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants (recruited in 1948–2012) from up to 17 studies per phenotype. Here we discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include 1) the software code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify, or extend these harmonizations to additional studies, and 2) the results of labeling thousands of phenotype variables with controlled vocabulary terms.

Large-scale gene sequencing studies for complex traits have the potential to identify causal genes with therapeutic implications. We performed gene-based association testing of blood lipid levels with rare (minor allele frequency170,000 individuals from multiple ancestries: 97,493 European, 30,025 South Asian, 16,507 African, 16,440 Hispanic/Latino, 10,420 East Asian, and 1,182 Samoan. We identified 35 genes associated with circulating lipid levels. Ten of these: ALB, SRSF2, JAK2, CREB3L3, TMEM136, VARS, NR1H3, PLA2G12A, PPARG and STAB1 have not been implicated for lipid levels using rare coding variation in population-based samples. We prioritize 32 genes identified in array-based genome-wide association study (GWAS) loci based on gene-based associations, of which three: EVI5, SH2B3, and PLIN1, had no prior evidence of rare coding variant associations. Most of the associated genes showed evidence of association in multiple ancestries. Also, we observed an enrichment of gene-based associations for low-density lipoprotein cholesterol drug target genes, and for genes closest to GWAS index single nucleotide polymorphisms (SNP). Our results demonstrate that gene-based associations can be beneficial for drug target development and provide evidence that the gene closest to the array-based GWAS index SNP is often the functional gene for blood lipid levels.

Pharmaceutical drugs targeting dyslipidemia and cardiovascular disease (CVD) may increase the risk of fatty liver disease and other metabolic disorders. To identify potential novel CVD drug targets without these adverse effects, we perform genome-wide analyses of participants in the HUNT Study in Norway (n = 69,479) to search for protein-altering variants with beneficial impact on quantitative blood traits related to cardiovascular disease, but without detrimental impact on liver function. We identify 76 (11 previously unreported) presumed causal protein-altering variants associated with one or more CVD- or liver-related blood traits. Nine of the variants are predicted to result in loss-of-function of the protein. This includes ZNF529:p.K405X, which is associated with decreased low-density-lipoprotein (LDL) cholesterol (P = 1.3 × 10−8) without being associated with liver enzymes or non-fasting blood glucose. Silencing of ZNF529 in human hepatoma cells results in upregulation of LDL receptor and increased LDL uptake in the cells. This suggests that inhibition of ZNF529 or its gene product should be prioritized as a novel candidate drug target for treating dyslipidemia and associated CVD., Drugs targeting cardiovascular disease (CVD) can have negative consequences for liver function. Here, the authors combine genome wide analyses on 69,479 individuals to identify loss-of-function variants with beneficial effects on CVD-related traits without negative impacts on liver function.

Whole genome sequencing (WGS), a powerful tool for detecting novel coding and non-coding disease-causing variants, has largely been applied to clinical diagnosis of inherited disorders. Here we leveraged WGS data in up to 62,653 ethnically diverse participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) program and assessed statistical association of variants with seven red blood cell (RBC) quantitative traits. We discovered 14 single variant-RBC trait associations at 12 genomic loci. Several of the RBC trait-variant associations (RPN1, ELL2, MIDN, HBB, HBA1, PIEZO1, G6PD) were replicated in independent GWAS datasets imputed to the TOPMed reference panel. Most of these newly discovered variants are rare/low frequency, and several are observed disproportionately among non-European Ancestry (African, Hispanic/Latino, or East Asian) populations. We identified a 3bp indel p.Lys2169del (common only in the Ashkenazi Jewish population) of PIEZO1, a gene responsible for the Mendelian red cell disorder hereditary xerocytosis [OMIM 194380], associated with higher MCHC. In stepwise conditional analysis and in gene-based rare variant aggregated association analysis, we identified several of the variants in HBB, HBA1, TMPRSS6, and G6PD that represent the carrier state for known coding, promoter, or splice site loss-of-function variants that cause inherited RBC disorders. Finally, we applied base and nuclease editing to demonstrate that the sentinel variant rs112097551 (nearest gene RPN1) acts through a cis-regulatory element that exerts long-range control of the gene RUVBL1 which is essential for hematopoiesis. Together, these results demonstrate the utility of WGS in ethnically-diverse population-based samples and gene editing for expanding knowledge of the genetic architecture of quantitative hematologic traits and suggest a continuum between complex trait and Mendelian red cell disorders.

MotivationWhole-genome DNA sequencing (WGS) enables the discovery of non-coding variants, but tools are lacking to prioritize the subset that functionally impacts human phenotypes. DNA sequence variants that disrupt or create transcription factor binding sites (TFBS) can modulate gene expression. find-tfbs efficiently scans phased WGS in large cohorts to identify and count TFBSs in regulatory sequences. This information can then be used in association testing to find putatively functional non-coding variants associated with complex human diseases or traits.ResultsWe applied find-tfbs to discover functional non-coding variants associated with hematological traits in the NHLBI Trans-Omics for Precision Medicine (TOPMed) WGS dataset (Nmax=44,709). We identified >2000 associations at P−9, implicating specific blood cell-types, transcription factors and causal genes. The vast majority of these associations are captured by variants identified in large genome-wide association studies (GWAS) for blood-cell traits. find-tfbs is computationally efficient and robust, allowing for the rapid identification of non-coding variants associated with multiple human phenotypes in very large sample size.Availabilityhttps://github.com/Helkafen/find-tfbs and https://github.com/Helkafen/find-tfbs-demoContactssebastian.meric.de.bellefon@umontreal.ca and guillaume.lettre@umontreal.caSupplementary informationSupplementary data are available.

Type 2 diabetes is increasing in all ancestry groups1. Part of its genetic basis may reside among the rare (minor allele frequency 2. We analyzed high-coverage (mean depth 38.2x) whole genome sequencing from 9,639 individuals with T2D and 34,994 controls in the NHLBI’s Trans-Omics for Precision Medicine (TOPMed) program2 to show that rare, non-coding variants that are poorly captured by genotyping arrays or imputation panels contribute h2=53% (P=4.2×10−5) to the genetic component of risk in the largest (European) ancestry subset. We coupled sequence variation with islet epigenomic signatures3 to annotate and group rare variants with respect to gene expression4, chromatin state5 and three-dimensional chromatin architecture6, and show that pancreatic islet regulatory elements contribute to T2D genetic risk (h2=8%, P=2.4×10−3). We used islet annotation to create a non-coding framework for rare variant aggregation testing. This approach identified five loci containing rare alleles in islet regulatory elements that suggest novel biological mechanisms readily linked to hypotheses about variant-to-function. Large scale whole genome sequence analysis reveals the substantial contribution of rare, non-coding variation to the genetic architecture of T2D and highlights the value of tissue-specific regulatory annotation for variant-to-function discovery.

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P = 8.5 × 10−72), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P = 1.7 × 10−4), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P = 1.4 × 10−5). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids., The influence of X chromosome genetic variation on blood lipids and coronary heart disease (CHD) is not well understood. Here, the authors analyse X chromosome sequencing data across 65,322 multi-ancestry individuals, identifying associations of the Xq23 locus with lipid changes and reduced risk of CHD and diabetes mellitus.

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 (PRS(AD): 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 (PRS(AD+): OR, 2.16; 95% CI, 1.89–2.47) and further improved when including FLG LOF mutations (PRS(AD++): OR, 3.23; 95% CI, 2.57–4.07). Importantly, while all 3 PRSs correlated with AD severity, the best prediction was from PRS(AD++), 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.

Introduction: Induced pluripotent stem cells (iPSC) have a significant capacity for self-renewal and can differentiate into any somatic cell type, such as megakaryocytes (MK) and vascular endothelial cells (EC). We have previously shown that iPSC-derived MKs (IPSC-MK) upregulate the gene expression levels of irreversible glycolytic enzymes committing glucose to glycolysis in comparison to their parental iPSC lines (Vaidya et al., 2016). This pattern differed from past comparisons between somatic source cells and iPSCs (Chung et al., 2007; Varum et al., 2011; Zhang et al., 2011). To further corroborate our data, we extended our analysis to iPSC-derived ECs (iPSC-EC). Methods: We studied expression of glycolytic enzyme genes in 30 human iPSC lines and paired iPSC-EC and iPSC-MK lines using RNA-seq. iPSC-EC to iPSC expression ratios for 11 glycolytic enzymes were estimated using a multilevel mixed-effect model regression, and a significance threshold of p < 0.05/11 = 0.0045 was used. A bivariate statistical analysis was used to assess the correlation between iPSC-EC and iPSC-MK data. Results: Of the 11 glycolytic enzymes studied, 4 were highly expressed in iPSC-ECs (Log2(iPSC-EC/iPSC) ≥ 1) while 1 was highly expressed in iPSCs (Log2(iPSC/iPSC-EC) ≥ 1). Transcripts for functionally irreversible enzymes including ADPGK, PFKP, and PKLR displayed higher expression levels in iPSC-ECs compared to iPSCs. Similarly, 6 glycolytic genes demonstrated higher expression in iPSC-MKs, while 2 were more highly expressed in iPSCs. The functionally irreversible enzymes HK1, ADPGK, and PFK exhibited higher expression levels in iPSC-MKs. The ratios of gene expression (derived-lines:iPSCs) for all 37 genes in the iPSC-MK results correlated with those of the iPSC-EC results: Pearson’s r = 0.52, p = 0.001. Conclusion: Both iPSC-ECs and iPSC-MKs upregulate the expression of glycolytic enzymes compared to their parental iPSCs. In light of data showing that differentiated cells are more metabolically active and have more mitochondria than iPSCs (Chandra et al., 2020), these results suggest that differentiation of iPSCs is accompanied by an increase in both glycolytic and TCA cycle activities, with glycolytic generation of pyruvate used for mitochondrial production of ATP.

Food allergy (FA), a growing public health burden in the United States, and familial aggregation studies support strong roles for both genes and environment in FA risk. Deepening our understanding of the molecular and cellular mechanisms driving FAs is paramount to improving its prevention, diagnosis, and clinical management. In this review, we document lessons learned from the genetics of FA that have aided our understanding of these mechanisms. Although current genetic association studies suffer from low power, heterogeneity in definition of FA, and difficulty in our ability to truly disentangle FA from food sensitization (FS) and general atopy genetics, they reveal a set of genetic loci, genes, and variants that continue to implicate the importance of barrier and immune function genes across the atopic march, and FA in particular. The largest reported effects on FA are from MALT1 (odds ratio, 10.99), FLG (average odds ratio, ∼2.9), and HLA (average odds ratio, ∼2.03). The biggest challenge in the field of FA genetics is to elucidate the specific mechanism of action on FA risk and pathogenesis for these loci, and integrative approaches including genetics/genomics with transcriptomics, proteomics, and metabolomics will be critical next steps to translating these genetic insights into practice.

Human induced pluripotent stem cell (hiPSC) lines have previously been generated through the NHLBI sponsored NextGen program at nine individual study sites. Here, we examined the structural integrity of 506 hiPSC lines as determined by copy number variations (CNVs). We observed that 149 hiPSC lines acquired 258 CNVs relative to donor DNA. We identified six recurrent regions of CNVs on chromosomes 1, 2, 3, 16 and 20 that overlapped with cancer associated genes. Furthermore, the genes mapping to regions of acquired CNVs show an enrichment in cancer related biological processes (IL6 production) and signaling cascades (JNK cascade & NFκB cascade). The genomic region of instability on chr20 (chr20q11.2) includes transcriptomic signatures for cancer associated genes such as ID1, BCL2L1, TPX2, PDRG1 and HCK. Of these HCK shows statistically significant differential expression between carrier and non-carrier hiPSC lines. Overall, while a low level of genomic instability was observed in the NextGen generated hiPSC lines, the observation of structural instability in regions with known cancer associated genes substantiates the importance of systematic evaluation of genetic variations in hiPSCs before using them as disease/research models.

Traditional Hardy-Weinberg equilibrium (HWE) tests (the χ2 test and the exact test) have long been used as a metric for evaluating genotype quality, as technical artifacts leading to incorrect genotype calls often can be identified as deviations from HWE. However, in datasets comprised of individuals from diverse ancestries, HWE can be violated even without genotyping error, complicating the use of HWE testing to assess genotype data quality. In this manuscript, we present the Robust Unified Test for HWE (RUTH) to test for HWE while accounting for population structure and genotype uncertainty, and evaluate the impact of population heterogeneity and genotype uncertainty on the standard HWE tests and alternative methods using simulated and real sequence datasets. Our results demonstrate that ignoring population structure or genotype uncertainty in HWE tests can inflate false positive rates by many orders of magnitude. Our evaluations demonstrate different tradeoffs between false positives and statistical power across the methods, with RUTH consistently amongst the best across all evaluations. RUTH is implemented as a practical and scalable software tool to rapidly perform HWE tests across millions of markers and hundreds of thousands of individuals while supporting standard VCF/BCF formats. RUTH is publicly available at https://www.github.com/statgen/ruth.

Genotype-phenotype association studies often combine phenotype data from multiple studies to increase power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data sharing mechanisms. This system was developed for the National Heart, Lung and Blood Institute’s Trans-Omics for Precision Medicine (TOPMed) program, which is generating genomic and other omics data for >80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants from up to 17 TOPMed studies per phenotype. We discuss the challenges faced in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled-access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include (1) the code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify or extend these harmonizations to additional studies; and (2) results of labeling thousands of phenotype variables with controlled vocabulary terms.

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 h 2 ), which suggest that variation in DNM rate is significantly shaped by nonadditive genetic effects and the environment.

Background Genome-wide association studies have identified multiple genomic loci associated with coronary artery disease, but most are common variants in non-coding regions that provide limited information on causal genes and etiology of the disease. To overcome the limited scope that common variants provide, we focused our investigation on low-frequency and rare sequence variations primarily residing in coding regions of the genome. Methods and results Using samples of individuals of European ancestry from ten cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium, both cross-sectional and prospective analyses were conducted to examine associations between genetic variants and myocardial infarction (MI), coronary heart disease (CHD), and all-cause mortality following these events. For prevalent events, a total of 27,349 participants of European ancestry, including 1831 prevalent MI cases and 2518 prevalent CHD cases were used. For incident cases, a total of 55,736 participants of European ancestry were included (3,031 incident MI cases and 5,425 incident CHD cases). There were 1,860 all-cause deaths among the 3,751 MI and CHD cases from six cohorts that contributed to the analysis of all-cause mortality. Single variant and gene-based analyses were performed separately in each cohort and then meta-analyzed for each outcome. A low-frequency intronic variant (rs988583) in PLCL1 was significantly associated with prevalent MI (OR = 1.80, 95% confidence interval: 1.43, 2.27; P = 7.12 × 10−7). We conducted gene-based burden tests for genes with a cumulative minor allele count (cMAC) ≥ 5 and variants with minor allele frequency (MAF) < 5%. TMPRSS5 and LDLRAD1 were significantly associated with prevalent MI and CHD, respectively, and RC3H2 and ANGPTL4 were significantly associated with incident MI and CHD, respectively. No loci were significantly associated with all-cause mortality following a MI or CHD event. Conclusion This study identified one known locus (ANGPTL4) and four new loci (PLCL1, RC3H2, TMPRSS5, and LDLRAD1) associated with cardiovascular disease risk that warrant further investigation.

Inhaled corticosteroids (ICS) are widely prescribed for patients with chronic obstructive pulmonary disease (COPD), yet have variable outcomes and adverse reactions, which may be genetically determined. The primary aim of the study was to identify the genetic determinants for forced expiratory volume in 1 s (FEV1) changes related to ICS therapy.In the Lung Health Study (LHS)-2, 1116 COPD patients were randomised to the ICS triamcinolone acetonide (n=559) or placebo (n=557) with spirometry performed every 6 months for 3 years. We performed a pharmacogenomic genome-wide association study for the genotype-by-ICS treatment effect on 3 years of FEV1 changes (estimated as slope) in 802 genotyped LHS-2 participants. Replication was performed in 199 COPD patients randomised to the ICS, fluticasone or placebo.A total of five loci showed genotype-by-ICS interaction at p−6; of these, single nucleotide polymorphism (SNP) rs111720447 on chromosome 7 was replicated (discovery p=4.8×10−6, replication p=5.9×10−5) with the same direction of interaction effect. ENCODE (Encyclopedia of DNA Elements) data revealed that in glucocorticoid-treated (dexamethasone) A549 alveolar cell line, glucocorticoid receptor binding sites were located near SNP rs111720447. In stratified analyses of LHS-2, genotype at SNP rs111720447 was significantly associated with rate of FEV1 decline in patients taking ICS (C allele β 56.36 mL·year−1, 95% CI 29.96–82.76 mL·year−1) and in patients who were assigned to placebo, although the relationship was weaker and in the opposite direction to that in the ICS group (C allele β −27.57 mL·year−1, 95% CI −53.27– −1.87 mL·year−1).The study uncovered genetic factors associated with FEV1 changes related to ICS in COPD patients, which may provide new insight on the potential biology of steroid responsiveness in COPD.

Lipoprotein(a), Lp(a), is a modified low-density lipoprotein particle that contains apolipoprotein(a), encoded by LPA, and is a highly heritable, causal risk factor for cardiovascular diseases that varies in concentrations across ancestries. Here, we use deep-coverage whole genome sequencing in 8392 individuals of European and African ancestry to discover and interpret both single-nucleotide variants and copy number (CN) variation associated with Lp(a). We observe that genetic determinants between Europeans and Africans have several unique determinants. The common variant rs12740374 associated with Lp(a) cholesterol is an eQTL for SORT1 and independent of LDL cholesterol. Observed associations of aggregates of rare non-coding variants are largely explained by LPA structural variation, namely the LPA kringle IV 2 (KIV2)-CN. Finally, we find that LPA risk genotypes confer greater relative risk for incident atherosclerotic cardiovascular diseases compared to directly measured Lp(a), and are significantly associated with measures of subclinical atherosclerosis in African Americans., Circulating lipoprotein(a) is an important risk factor for cardiovascular disease and shows variability between different ethnic groups. Here, Zekavat et al. perform whole-genome sequencing in individuals of European and African ancestries and find ancestry-specific genetic determinants for lipoprotein(a) levels.

Coronary artery disease (CAD) remains a major cause of mortality and morbidity worldwide. The aggregation of activated platelets on a ruptured atherosclerotic plaque is a critical step in most acute cardiovascular events like myocardial infarction. Platelet aggregation both at baseline and after aspirin is highly heritable. Genome-wide association studies (GWAS) have identified a common variant within the first intron of the platelet endothelial aggregation receptor1 (PEAR1), to be robustly associated with platelet aggregation. In this study, we used targeted deep sequencing to fine-map the prior GWAS peak and identify additional rare variants of PEAR1 that account for missing heritability in platelet aggregation within the GeneSTAR families. In this study, 1709 subjects (1043 European Americans, EA and 666 African Americans, AA) from families in the GeneSTAR study were included. In vitro platelet aggregation in response to collagen, ADP and epinephrine was measured at baseline and 14 days after aspirin therapy (81 mg/day). Targeted deep sequencing of PEAR1 in addition to 2kb of upstream and downstream of the gene was performed. Under an additive genetic model, the association of single variants of PEAR1 with platelet aggregation phenotypes were examined. Additionally, we examined the association between the burden of PEAR1 rare non-synonymous variants and platelet aggregation phenotypes. Of 532 variants identified through sequencing, the intron 1 variant, rs12041331, was significantly associated with all platelet aggregation phenotypes at baseline and after platelet inhibition with aspirin therapy. rs12566888, which is in linkage disequilibrium with rs12041331, was associated with platelet aggregation phenotypes but to a lesser extent. In the EA families, the burden of PEAR1 missense variants was associated with platelet aggregation after aspirin therapy when the platelets were stimulated with epinephrine (p = 0.0009) and collagen (p = 0.03). In AAs, the burden of PEAR1 missense variants was associated, to a lesser degree, with platelet aggregation in response to epinephrine (p = 0.02) and ADP (p = 0.04). Our study confirmed that the GWAS-identified variant, rs12041331, is the strongest variant associated with platelet aggregation both at baseline and after aspirin therapy in our GeneSTAR families in both races. We identified additional association of rare missense variants in PEAR1 with platelet aggregation following aspirin therapy. However, we observed a racial difference in the contribution of these rare variants to the platelet aggregation, most likely due to higher residual missing heritability of platelet aggregation after accounting for rs12041331 in the EAs compared to AAs.

Chronic obstructive pulmonary disease (COPD) is characterized by reduced lung function and is the third leading cause of death globally. Through genome-wide association discovery in 48,943 individuals, selected from extremes of the lung function distribution in UK Biobank, and follow-up in 95,375 individuals, we increased the yield of independent signals for lung function from 54 to 97. A genetic risk score was associated with COPD susceptibility (odds ratio per 1 s.d. of the risk score (similar to 6 alleles) (95% confidence interval) = 1.24 (1.20-1.27), P = 5.05 x 10(-49)), and we observed a 3.7-fold difference in COPD risk between individuals in the highest and lowest genetic risk score deciles in UK Biobank. The 97 signals show enrichment in genes for development, elastic fibers and epigenetic regulation pathways. We highlight targets for drugs and compounds in development for COPD and asthma (genes in the inositol phosphate metabolism pathway and CHRM3) and describe targets for potential drug repositioning from other clinical indications.

SummaryInhibition of platelet reactivity is a common therapeutic strategy in secondary prevention of cardiovascular disease. Genetic and environmental factors influence inter-individual variation in platelet reactivity. Identifying genes that contribute to platelet reactivity can reveal new biological mechanisms and possible therapeutic targets. Here, we examined rare coding variation to identify genes associated with platelet reactivity in a population-based cohort. To do so, we performed whole exome sequencing in the Framingham Heart Study and conducted single variant and gene-based association tests against platelet reactivity to collagen, adenosine diphosphate (ADP), and epinephrine agonists in up to 1,211 individuals. Single variant tests revealed no significant associations (pSupplementary Material to this article is available online at www.thrombosis-online.com.

Motivation Variant calling from next-generation sequencing (NGS) data is susceptible to false positive calls due to sequencing, mapping and other errors. To better distinguish true from false positive calls, we present a method that uses genotype array data from the sequenced samples, rather than public data such as HapMap or dbSNP, to train an accurate classifier using Random Forests. We demonstrate our method on a set of variant calls obtained from 642 African-ancestry genomes from the Consortium on Asthma among African-ancestry Populations in the Americas (CAAPA), sequenced to high depth (30X). Results We have applied our classifier to compare call sets generated with different calling methods, including both single-sample and multi-sample callers. At a False Positive Rate of 5%, our method determines true positive rates of 97.5%, 95% and 99% on variant calls obtained using Illuminas single-sample caller CASAVA, Real Time Genomics multisample variant caller, and the GATK UnifiedGenotyper, respectively. Since NGS sequencing data may be accompanied by genotype data for the same samples, either collected concurrent to sequencing or from a previous study, our method can be trained on each dataset to provide a more accurate computational validation of site calls compared to generic methods. Moreover, our method allows for adjustment based on allele frequency (e.g. a different set of criteria to determine quality for rare versus common variants) and thereby provides insight into sequencing characteristics that indicate call quality for variants of different frequencies. Availability and Implementation Code is available on Github at: https://github.com/suyashss/variant_validation Supplementary information Supplementary data are available at Bioinformatics online.

Age is the dominant risk factor for most chronic human diseases; yet the mechanisms by which aging confers this risk are largely unknown.1 Recently, the age-related acquisition of somatic mutations in regenerating hematopoietic stem cell populations was associated with both hematologic cancer incidence2–4 and coronary heart disease prevalence.5 Somatic mutations with leukemogenic potential may confer selective cellular advantages leading to clonal expansion, a phenomenon termed ‘Clonal Hematopoiesis of Indeterminate Potential’ (CHIP).6 Simultaneous germline and somatic whole genome sequence analysis now provides the opportunity to identify root causes of CHIP. Here, we analyze high-coverage whole genome sequences from 97,691 participants of diverse ancestries in the NHLBI TOPMed program and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid, and inflammatory traits specific to different CHIP genes. Association of a genome-wide set of germline genetic variants identified three genetic loci associated with CHIP status, including one locus at TET2 that was African ancestry specific. In silico-informed in vitro evaluation of the TET2 germline locus identified a causal variant that disrupts a TET2 distal enhancer. Aggregates of rare germline loss-of-function variants in CHEK2, a DNA damage repair gene, predisposed to CHIP acquisition. Overall, we observe that germline genetic variation altering hematopoietic stem cell function and the fidelity of DNA-damage repair increase the likelihood of somatic mutations leading to CHIP.

© 2020, The Author(s), under exclusive licence to Springer Nature Limited. Age is the dominant risk factor for most chronic human diseases, but the mechanisms through which ageing confers this risk are largely unknown1. The age-related acquisition of somatic mutations that lead to clonal expansion in regenerating haematopoietic stem cell populations has recently been associated with both haematological cancer2–4 and coronary heart disease5—this phenomenon is termed clonal haematopoiesis of indeterminate potential (CHIP)6. Simultaneous analyses of germline and somatic whole-genome sequences provide the opportunity to identify root causes of CHIP. Here we analyse high-coverage whole-genome sequences from 97,691 participants of diverse ancestries in the National Heart, Lung, and Blood Institute Trans-omics for Precision Medicine (TOPMed) programme, and identify 4,229 individuals with CHIP. We identify associations with blood cell, lipid and inflammatory traits that are specific to different CHIP driver genes. Association of a genome-wide set of germline genetic variants enabled the identification of three genetic loci associated with CHIP status, including one locus at TET2 that was specific to individuals of African ancestry. In silico-informed in vitro evaluation of the TET2 germline locus enabled the identification of a causal variant that disrupts a TET2 distal enhancer, resulting in increased self-renewal of haematopoietic stem cells. Overall, we observe that germline genetic variation shapes haematopoietic stem cell function, leading to CHIP through mechanisms that are specific to clonal haematopoiesis as well as shared mechanisms that lead to somatic mutations across tissues.

Author(s): Daya, Michelle; Rafaels, Nicholas; Brunetti, Tonya M; Chavan, Sameer; Levin, Albert M; Shetty, Aniket; Gignoux, Christopher R; Boorgula, Meher Preethi; Wojcik, Genevieve; Campbell, Monica; Vergara, Candelaria; Torgerson, Dara G; Ortega, Victor E; Doumatey, Ayo; Johnston, Henry Richard; Acevedo, Nathalie; Araujo, Maria Ilma; Avila, Pedro C; Belbin, Gillian; Bleecker, Eugene; Bustamante, Carlos; Caraballo, Luis; Cruz, Alvaro; Dunston, Georgia M; Eng, Celeste; Faruque, Mezbah U; Ferguson, Trevor S; Figueiredo, Camila; Ford, Jean G; Gan, Weiniu; Gourraud, Pierre-Antoine; Hansel, Nadia N; Hernandez, Ryan D; Herrera-Paz, Edwin Francisco; Jimenez, Silvia; Kenny, Eimear E; Knight-Madden, Jennifer; Kumar, Rajesh; Lange, Leslie A; Lange, Ethan M; Lizee, Antoine; Maul, Pissamai; Maul, Trevor; Mayorga, Alvaro; Meyers, Deborah; Nicolae, Dan L; O'Connor, Timothy D; Oliveira, Ricardo Riccio; Olopade, Christopher O; Olopade, Olufunmilayo; Qin, Zhaohui S; Rotimi, Charles; Vince, Nicolas; Watson, Harold; Wilks, Rainford J; Wilson, James G; Salzberg, Steven; Ober, Carole; Burchard, Esteban G; Williams, L Keoki; Beaty, Terri H; Taub, Margaret A; Ruczinski, Ingo; Mathias, Rasika A; Barnes, Kathleen C; CAAPA | Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Publisher's version (útgefin grein)., Brain lobar volumes are heritable but genetic studies are limited. We performed genome-wide association studies of frontal, occipital, parietal and temporal lobe volumes in 16,016 individuals, and replicated our findings in 8,789 individuals. We identified six genetic loci associated with specific lobar volumes independent of intracranial volume. Two loci, associated with occipital (6q22.32) and temporal lobe volume (12q14.3), were previously reported to associate with intracranial and hippocampal volume, respectively. We identified four loci previously unknown to affect brain volumes: 3q24 for parietal lobe volume, and 1q22, 4p16.3 and 14q23.1 for occipital lobe volume. The associated variants were located in regions enriched for histone modifications (DAAM1 and THBS3), or close to genes causing Mendelian brain-related diseases (ZIC4 and FGFRL1). No genetic overlap between lobar volumes and neurological or psychiatric diseases was observed. Our findings reveal part of the complex genetics underlying brain development and suggest a role for regulatory regions in determining brain volumes., 23Laboratory of Epidemiology and Population Sciences, National Institute on Aging, Intramural Research Program, National Institutes of Health, Bethesda, MD 20892, USA. 24Research Unit-Genetic Epidemiology, Gottfried Schatz Research Centre for Cell Signaling, Metabolism and Aging, Molecular Biology and Biochemistry, Medical University of Graz, 8010 Graz, Austria. 25Department of Neurology, University of Pittsburgh, Pittsburgh, PA 15260, USA. 26Department of Biostatistics, School of Public Health, Boston University, Boston, MA 02118, USA. 27Department of Radiology, Leiden University Medical Center, Leiden 2333ZA, the Netherlands. 28Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto M4G 1R8, Canada. 29Departments of Psychology and Psychiatry, University of Toronto, Toronto M5S 1A1, Canada.

Background Although epigenetic mechanisms are important risk factors for allergic disease, few studies have evaluated DNA methylation differences associated with atopic dermatitis (AD), and none has focused on AD with eczema herpeticum (ADEH+). We will determine how methylation varies in AD individuals with/without EH and associated traits. We modeled differences in genome-wide DNA methylation in whole blood cells from 90 ADEH+, 83 ADEH−, and 84 non-atopic, healthy control subjects, replicating in 36 ADEH+, 53 ADEH−, and 55 non-atopic healthy control subjects. We adjusted for cell-type composition in our models and used genome-wide and candidate-gene approaches. Results We replicated one CpG which was significantly differentially methylated by severity, with suggestive replication at four others showing differential methylation by phenotype or severity. Not adjusting for eosinophil content, we identified 490 significantly differentially methylated CpGs (ADEH+ vs healthy controls, genome-wide). Many of these associated with severity measures, especially eosinophil count (431/490 sites). Conclusions We identified a CpG in IL4 associated with serum tIgE levels, supporting a role for Th2 immune mediating mechanisms in AD. Changes in eosinophil level, a measure of disease severity, are associated with methylation changes, providing a potential mechanism for phenotypic changes in immune response-related traits. Electronic supplementary material The online version of this article (10.1186/s13148-019-0714-1) contains supplementary material, which is available to authorized users.

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

Hemoglobin A1c (HbA1c) is widely used to diagnose diabetes and assess glycemic control in patients with diabetes. However, nonglycemic determinants, including genetic variation, may influence how accurately HbA1c reflects underlying glycemia. Analyzing the NHLBI Trans-Omics for Precision Medicine (TOPMed) sequence data in 10,338 individuals from five studies and four ancestries (6,158 Europeans, 3,123 African-Americans, 650 Hispanics and 407 East Asians), we confirmed five regions associated with HbA1c (GCK in Europeans and African-Americans, HK1 in Europeans and Hispanics, FN3K/FN3KRP in Europeans and G6PD in African-Americans and Hispanics) and discovered a new African-ancestry specific low-frequency variant (rs1039215 in HBG2/HBE1, minor allele frequency (MAF)=0.03). The most associated G6PD variant (p.Val98Met, rs1050828-T, MAF=12% in African-Americans, MAF=2% in Hispanics) lowered HbA1c (−0.88% in hemizygous males, −0.34% in heterozygous females) and explained 23% of HbA1c variance in African-Americans and 4% in Hispanics. Additionally, we identified a rare distinct G6PD coding variant (rs76723693 - p.Leu353Pro, MAF=0.5%; −0.98% in hemizygous males, −0.46% in heterozygous females) and detected significant association with HbA1c when aggregating rare missense variants in G6PD. We observed similar magnitude and direction of effects for rs1039215 (HBG2) and rs76723693 (G6PD) in the two largest TOPMed African-American cohorts and replicated the rs76723693 association in the UK Biobank African-ancestry participants. These variants in G6PD and HBG2 were monomorphic in the European and Asian samples. African or Hispanic ancestry individuals carrying G6PD variants may be underdiagnosed for diabetes when screened with HbA1c. Thus, assessment of these variants should be considered for incorporation into precision medicine approaches for diabetes diagnosis.