Ever larger Structural Variant (SV) catalogs highlighting the diversity within and between populations help researchers better understand the links between SVs and disease. The identification of SVs from DNA sequence data is non-trivial and requires a balance between comprehensiveness and precision. Here we present a catalog of 355,667 SVs (59.34% novel) across autosomes and the X chromosome (50bp+) from 138,134 individuals in the diverse TOPMed consortium. We describe our methodologies for SV inference resulting in high variant quality and >90% allele concordance compared to long-read de-novo assemblies of well-characterized control samples. We demonstrate utility through significant associations between SVs and important various cardio-metabolic and hemotologic traits. We have identified 690 SV hotspots and deserts and those that potentially impact the regulation of medically relevant genes. This catalog characterizes SVs across multiple populations and will serve as a valuable tool to understand the impact of SV on disease development and progression.

The second-generation antipsychotic quetiapine is commonly used off-label for its anxiolytic and hypnotic properties. However, quetiapine is associated with problematic side-effects. We used Danish Medicinal Product Statistics and a 20% random sample of the Danish population's prescription fills (2001-2020) to describe the utilization of quetiapine and proportion of various prescriber types (general practitioner [GP], specialist in private practice, hospital physician and other prescribers) both in connection to first-time and subsequent prescriptions. In 2020, 92% of all quetiapine was dispensed outside hospitals and the average daily dispensed quantity of quetiapine per user corresponded to 100 mg/user/d. A GP issued 53% of first-time prescriptions and 75% of subsequent prescriptions for quetiapine in 2020. The proportion of quetiapine prescriptions issued by GPs varied by age group-from 14% among 0-17-year-olds to 93% among the ≥80-year-olds. Future initiatives on the rational use of quetiapine and related drugs, especially among adults, should target GPs.

The electrocardiographic PR interval reflects atrioventricular conduction, and is associated with conduction abnormalities, pacemaker implantation, atrial fibrillation (AF), and cardiovascular mortality. Here we report a multi-ancestry (N = 293,051) genome-wide association meta-analysis for the PR interval, discovering 202 loci of which 141 have not previously been reported. Variants at identified loci increase the percentage of heritability explained, from 33.5% to 62.6%. We observe enrichment for cardiac muscle developmental/contractile and cytoskeletal genes, highlighting key regulation processes for atrioventricular conduction. Additionally, 8 loci not previously reported harbor genes underlying inherited arrhythmic syndromes and/or cardiomyopathies suggesting a role for these genes in cardiovascular pathology in the general population. We show that polygenic predisposition to PR interval duration is an endophenotype for cardiovascular disease, including distal conduction disease, AF, and atrioventricular pre-excitation. These findings advance our understanding of the polygenic basis of cardiac conduction, and the genetic relationship between PR interval duration and cardiovascular disease.

During clinical trials, mirabegron, a β3-adrenoreceptor agonist, was associated with increased vital signs vs placebo in patients with overactive bladder.

Ever larger Structural Variant (SV) catalogs highlighting the diversity within and between populations help researchers better understand the links between SVs and disease. The identification of SVs from DNA sequence data is non-trivial and requires a balance between comprehensiveness and precision. Here we present a catalog of 355,667 SVs (59.34% novel) across autosomes and the X chromosome (50bp+) from 138,134 individuals in the diverse TOPMed consortium. We describe our methodologies for SV inference resulting in high variant quality and >90% allele concordance compared to long-read de-novo assemblies of well-characterized control samples. We demonstrate utility through significant associations between SVs and important various cardio-metabolic and hematologic traits. We have identified 690 SV hotspots and deserts and those that potentially impact the regulation of medically relevant genes. This catalog characterizes SVs across multiple populations and will serve as a valuable tool to understand the impact of SV on disease development and progression.

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

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

Research increasingly relies on interrogating large-scale data resources. The NIH National Heart, Lung, and Blood Institute developed the NHLBI BioData CatalystⓇ (BDC), a community-driven ecosystem where researchers, including bench and clinical scientists, statisticians, and algorithm developers, find, access, share, store, and compute on large-scale datasets. This ecosystem provides secure, cloud-based workspaces, user authentication and authorization, search, tools and workflows, applications, and new innovative features to address community needs, including exploratory data analysis, genomic and imaging tools, tools for reproducibility, and improved interoperability with other NIH data science platforms. BDC offers straightforward access to large-scale datasets and computational resources that support precision medicine for heart, lung, blood, and sleep conditions, leveraging separately developed and managed platforms to maximize flexibility based on researcher needs, expertise, and backgrounds. Through the NHLBI BioData Catalyst Fellows Program, BDC facilitates scientific discoveries and technological advances. BDC also facilitated accelerated research on the coronavirus disease-2019 (COVID-19) pandemic.

Platelets play an essential role in hemostasis and thrombosis. We performed a genome-wide association study of platelet count in 12,491 participants of the Hispanic Community Health Study/Study of Latinos by using a mixed-model method that accounts for admixture and family relationships. We discovered and replicated associations with five genes (ACTN1, ETV7, GABBR1-MOG, MEF2C, and ZBTB9-BAK1). Our strongest association was with Amerindian-specific variant rs117672662 (p value = 1.16 × 10(-28)) in ACTN1, a gene implicated in congenital macrothrombocytopenia. rs117672662 exhibited allelic differences in transcriptional activity and protein binding in hematopoietic cells. Our results underscore the value of diverse populations to extend insights into the allelic architecture of complex traits.

Low-dose aspirin has been hypothesized to prevent cancer risk by inhibiting platelet aggregation. However, the anti-cancer effect of low-dose aspirin has recently been questioned and its effect on breast cancer development remains unclear. The impact of other antiplatelet drugs on breast cancer risk has rarely been evaluated. Thus, this study aimed to investigate the associations between breast cancer risk and antiplatelet drug use in a nationwide nested case-control study. From the Danish healthcare registries, we identified as cases all women with invasive breast cancer diagnosis between 2001 and 2018 (n = 68 852). The date of diagnosis corresponded to the index date. We matched cases to 10 population controls on age and calendar time, using risk set sampling. Controls were assigned the same index date as their matched case. We used the prescription registry to identify exposure to low-dose aspirin, clopidogrel and dipyridamole. We defined ever use of antiplatelet drugs as at least two prescriptions filled up to 1 year before the index date. We applied conditional logistic regression to calculate odds ratios (ORs) and 95% confidence intervals for breast cancer associated with the use of antiplatelet drugs, overall, by breast cancer subtype and by cumulative dose. Twelve percent of women had ever been exposed to low-dose aspirin, 2% to clopidogrel and 2% to dipyridamole. In multivariable models, breast cancer risk was not associated with ever use of low-dose aspirin (OR = 1.00 [0.97-1.03]), clopidogrel (OR = 0.93 [0.87-1.00]), and dipyridamole (OR = 1.02 [0.94-1.10]), compared with never use, and there was no evidence of a dose-response relation. However, we found an inverse association between dipyridamole use and breast cancer risk among women aged <55 years old, with suggestion of a dose-response relationship (OR per 1000 Defined Daily Doses = 0.72 [0.54-0.95]). Associations did not differ by breast cancer histological type, estrogen receptor status or clinical stage at diagnosis. Overall, the findings from this study do not support the use of antiplatelet drugs for breast cancer prevention.