Colocalization analysis of 3' UTR alternative polyadenylation quantitative trait loci reveals novel mechanisms underlying associations with lung function.

While many disease-associated single nucleotide polymorphisms (SNPs) are expression quantitative trait loci (eQTLs), a large proportion of genome-wide association study (GWAS) variants are of unknown function. Alternative polyadenylation (APA) plays an important role in posttranscriptional regulation by allowing genes to shorten or extend 3' untranslated regions (UTRs). We hypothesized that genetic variants that affect APA in lung tissue may lend insight into the function of respiratory associated GWAS loci. We generated alternative polyadenylation (apa) QTLs using RNA sequencing and whole genome sequencing on 1241 subjects from the Lung Tissue Research Consortium (LTRC) as part of the NHLBI TOPMed project. We identified 56 179 APA sites corresponding to 13 582 unique genes after filtering out APA sites with low usage. We found that a total of 8831 APA sites were associated with at least one SNP with q-value < 0.05. The genomic distribution of lead APA SNPs indicated that the majority are intronic variants (33%), followed by downstream gene variants (26%), 3' UTR variants (17%), and upstream gene variants (within 1 kb region upstream of transcriptional start site, 10%). APA sites in 193 genes colocalized with GWAS data for at least one phenotype. Genes containing the top APA sites associated with GWAS variants include membrane associated ring-CH-type finger 2 (MARCHF2), nectin cell adhesion molecule 2 (NECTIN2), and butyrophilin subfamily 3 member A2 (BTN3A2). Overall, these findings suggest that APA may be an important mechanism for genetic variants in lung function and chronic obstructive pulmonary disease (COPD).

Polygenic transcriptome risk scores for COPD and lung function improve cross-ethnic portability of prediction in the NHLBI TOPMed program.

While polygenic risk scores (PRSs) enable early identification of genetic risk for chronic obstructive pulmonary disease (COPD), predictive performance is limited when the discovery and target populations are not well matched. Hypothesizing that the biological mechanisms of disease are shared across ancestry groups, we introduce a PrediXcan-derived polygenic transcriptome risk score (PTRS) to improve cross-ethnic portability of risk prediction. We constructed the PTRS using summary statistics from application of PrediXcan on large-scale GWASs of lung function (forced expiratory volume in 1 s [FEV1] and its ratio to forced vital capacity [FEV1/FVC]) in the UK Biobank. We examined prediction performance and cross-ethnic portability of PTRS through smoking-stratified analyses both on 29,381 multi-ethnic participants from TOPMed population/family-based cohorts and on 11,771 multi-ethnic participants from TOPMed COPD-enriched studies. Analyses were carried out for two dichotomous COPD traits (moderate-to-severe and severe COPD) and two quantitative lung function traits (FEV1 and FEV1/FVC). While the proposed PTRS showed weaker associations with disease than PRS for European ancestry, the PTRS showed stronger association with COPD than PRS for African Americans (e.g., odds ratio [OR] = 1.24 [95% confidence interval [CI]: 1.08-1.43] for PTRS versus 1.10 [0.96-1.26] for PRS among heavy smokers with ≥ 40 pack-years of smoking) for moderate-to-severe COPD. Cross-ethnic portability of the PTRS was significantly higher than the PRS (paired t test p < 2.2 × 10-16 with portability gains ranging from 5% to 28%) for both dichotomous COPD traits and across all smoking strata. Our study demonstrates the value of PTRS for improved cross-ethnic portability compared to PRS in predicting COPD risk.

A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables.

The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since a.) statistical power is often limited and b.) modeling of environmental effects is nontrivial and such model misspecifications can lead to false positive interaction findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy can increase the power to detect interactions, identifying contributing genes and pathways is difficult based on these relatively global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate sets of genetic variants and/or multiple environmental factors. Based on the user's choice of statistical/machine learning approaches, a screening step selects and combines potential interactions into scores with improved interpretability. In the testing step, the application of robust statistics minimizes the susceptibility to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios, and we show how the screening strategy influences statistical power. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified highly significant interactions based on subcomponents of genetic risk scores. While the contributing single variant interaction signals are weak, our results indicate interaction patterns that result in strong aggregated effects, providing potential insights into underlying gene-environment interaction mechanisms.

Assessing the contribution of rare genetic variants to phenotypes of chronic obstructive pulmonary disease using whole-genome sequence data.

RATIONALE: Genetic variation has a substantial contribution to chronic obstructive pulmonary disease (COPD) and lung function measurements. Heritability estimates using genome-wide genotyping data can be biased if analyses do not appropriately account for the nonuniform distribution of genetic effects across the allele frequency and linkage disequilibrium (LD) spectrum. In addition, the contribution of rare variants has been unclear. OBJECTIVES: We sought to assess the heritability of COPD and lung function using whole-genome sequence data from the Trans-Omics for Precision Medicine program. METHODS: Using the genome-based restricted maximum likelihood method, we partitioned the genome into bins based on minor allele frequency and LD scores and estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio in 11 051 European ancestry and 5853 African-American participants. MEASUREMENTS AND MAIN RESULTS: In European ancestry participants, the estimated heritability of COPD, FEV1% predicted and FEV1/FVC ratio were 35.5%, 55.6% and 32.5%, of which 18.8%, 19.7%, 17.8% were from common variants, and 16.6%, 35.8%, and 14.6% were from rare variants. These estimates had wide confidence intervals, with common variants and some sets of rare variants showing a statistically significant contribution (P-value < 0.05). In African-Americans, common variant heritability was similar to European ancestry participants, but lower sample size precluded calculation of rare variant heritability. CONCLUSIONS: Our study provides updated and unbiased estimates of heritability for COPD and lung function, and suggests an important contribution of rare variants. Larger studies of more diverse ancestry will improve accuracy of these estimates.

Comparison of goserelin and leuprorelin for ovarian protection during chemotherapy in young patients with breast cancer.

PURPOSE: Although different gonadotropin-releasing hormone (GnRH) agonists may have different effects, their effect of ovarian protection during chemotherapy for breast cancer has not been compared. This study aimed to compare the effects of goserelin and leuprorelin for ovarian protection during chemotherapy in young patients with breast cancer. METHODS: This prospective study analyzed 193 patients with breast cancer aged ≤ 40 years who had regular menstruation and serum anti-Müllerian hormone (AMH) levels ≥ 1 ng/mL before treatment. Patients received either goserelin or leuprorelin for ovarian protection during doxorubicin/cyclophosphamide-based chemotherapy. Resumption of menstruation and changes in serum levels of AMH were compared between the two groups at 12 months after completion of chemotherapy. RESULTS: The mean age and the pretreatment serum AMH level were 33.2 years and 4.4 ng/mL in goserelin group and 34.2 years and 4.0 ng/mL in leuprorelin group. The proportion of patients who resumed menstruation was not different between the goserelin (94.4%) and leuprorelin (95.3%) groups at 12 months after chemotherapy completion. Serum AMH levels decreased significantly in both the goserelin (from 4.4 to 1.2 ng/mL) and leuprorelin (from 4.0 to 1.2 ng/mL) groups, with no statistical significance. In addition, no difference was found in the proportion of patients with serum AMH levels ≥ 1 ng/mL between the goserelin (49.5%) and leuprorelin (44.2%) groups at 12 months after chemotherapy. CONCLUSION: Goserelin and leuprorelin were comparable in terms of ovarian protection during doxorubicin/cyclophosphamide-based chemotherapy in young patients with breast cancer.

C FTR variants are associated with chronic bronchitis in smokers.

INTRODUCTION: Loss-of-function variants in both copies of the cystic fibrosis transmembrane conductance regulator (CFTR) gene cause cystic fibrosis (CF); however, there is evidence that reduction in CFTR function due to the presence of one deleterious variant can have clinical consequences. Here, we hypothesise that CFTR variants in individuals with a history of smoking are associated with chronic obstructive pulmonary disease (COPD) and related phenotypes. METHODS: Whole-genome sequencing was performed through the National Heart, Lung, and Blood Institute TOPMed (TransOmics in Precision Medicine) programme in 8597 subjects from the COPDGene (Genetic Epidemiology of COPD) study, an observational study of current and former smokers. We extracted clinically annotated CFTR variants and performed single-variant and variant-set testing for COPD and related phenotypes. Replication was performed in 2118 subjects from the ECLIPSE (Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints) study. RESULTS: We identified 301 coding variants within the CFTR gene boundary: 147 of these have been reported in individuals with CF, including 36 CF-causing variants. We found that CF-causing variants were associated with chronic bronchitis in variant-set testing in COPDGene (one-sided p=0.0025; OR 1.53) and in meta-analysis of COPDGene and ECLIPSE (one-sided p=0.0060; OR 1.52). Single-variant testing revealed that the F508del variant was associated with chronic bronchitis in COPDGene (one-sided p=0.015; OR 1.47). In addition, we identified 32 subjects with two or more CFTR variants on separate alleles and these subjects were enriched for COPD cases (p=0.010). CONCLUSIONS: Cigarette smokers who carry one deleterious CFTR variant have higher rates of chronic bronchitis, while presence of two CFTR variants may be associated with COPD. These results indicate that genetically mediated reduction in CFTR function contributes to COPD related phenotypes, in particular chronic bronchitis.

A novel locus for exertional dyspnoea in childhood asthma.

Most children diagnosed with asthma have respiratory symptoms such as cough, dyspnoea and wheezing, which are also important markers of overall respiratory function. A decade of genome-wide association studies (GWAS) have investigated genetic susceptibility to asthma itself, but few have focused on important respiratory symptoms that characterise childhood asthma.Using whole-genome sequencing (WGS) data for 894 asthmatic trios from a Costa Rican cohort, we performed family-based association tests (FBATs) to assess the association between genetic variants and multiple asthma-relevant respiratory phenotypes: cough, phlegm, wheezing, exertional dyspnoea and exertional chest tightness. We tested whether genome-wide significant associations were replicated in two additional studies: 1) 286 asthmatic trios from the Childhood Asthma Management Program (CAMP), and 2) 2691 African American current or former smokers from the COPDGene study.In the 894 Costa Rican trios, we identified a genome-wide significant association (p=2.16×10-9) between exertional dyspnoea and the single nucleotide polymorphism (SNP) rs10165869, located on chromosome 2q37.3, that was replicated in the CAMP cohort (p=0.023) with the same direction of association (combined p=3.28×10-10). This association was not found in the African American participants from COPDGene. We also found suggestive evidence for an association between SNP rs10165869 and the atypical chemokine receptor 3 (ACKR3).Our finding encourages the secondary association analysis of a wider range of phenotypes that characterise respiratory symptoms in other airway diseases/studies.

Correction: Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations.

[This corrects the article DOI: 10.1371/journal.pgen.1006728.].

Psychophysiological Response According to the Greenness Index of Subway Station Space.

This study proposed a plan for implementing a pleasant and healthy indoor landscape in subway station space. To this end, it established a 3D landscape model of the subway interior by reviewing previous studies on indoor landscape and the greenness index of indoor spaces. Moreover, it investigated and analyzed psychophysiological responses of users to environmental indoor landscape design in subway station space. Subway stations were classified as underground subway stations and ground subway stations according to the presence of natural light inflow. The greenness index of indoor spaces was also divided into four types of 0%, 10%, 15%, and 20%. Through this process, eight 3D landscape models of the subway interior were implemented. In addition, this study investigated psychophysiological responses of 60 male and female adults in their 20 s and 30 s using the models implemented. The investigation result was analyzed based on a frequency analysis, the χ2 test, T-test, one-way analysis of variance, and multidimensional scaling, which were performed in SPSS Statistics 25. The results of this study can be summarized as follows. First, physiological responses of research subjects were analyzed based on their prefrontal α wave asymmetric values. The analytic result showed that the environment where interior landscape was adopted produced more positive effects than the environment where interior landscape was not adopted. Second, psychological responses of research subjects were examined based on their greenness index preference, awareness of interior landscape area, attention restoration effect, and space images. The analytic result indicated that, among eight 3D landscape models of the subway interior, they preferred the model with the greenness index of 15% for underground subway stations. In addition, they preferred the model with the greenness index of 10% the most for ground subway stations.

Heritability estimation of dichotomous phenotypes using a liability threshold model on ascertained family-based samples.

Numerous methods for estimating heritability have been proposed; however, unlike quantitative phenotypes, heritability estimation for dichotomous phenotypes is computationally and statistically complex, and the use of heritability is infrequent. In this study, we developed a statistical method to estimate heritability of dichotomous phenotypes using a liability threshold model in the context of ascertained family-based samples. This model assumes that dichotomous phenotypes are determined by unobserved latent variables that are normally distributed and can be applied to general pedigree data. The proposed methods were applied to simulated data and Korean type-2 diabetes family-based samples, and the accuracy of the estimates provided by the experimental methods was compared with that of the established methods.

Single-trait and multi-trait genome-wide association analyses identify novel loci for blood pressure in African-ancestry populations.

Hypertension is a leading cause of global disease, mortality, and disability. While individuals of African descent suffer a disproportionate burden of hypertension and its complications, they have been underrepresented in genetic studies. To identify novel susceptibility loci for blood pressure and hypertension in people of African ancestry, we performed both single and multiple-trait genome-wide association analyses. We analyzed 21 genome-wide association studies comprised of 31,968 individuals of African ancestry, and validated our results with additional 54,395 individuals from multi-ethnic studies. These analyses identified nine loci with eleven independent variants which reached genome-wide significance (P < 1.25×10-8) for either systolic and diastolic blood pressure, hypertension, or for combined traits. Single-trait analyses identified two loci (TARID/TCF21 and LLPH/TMBIM4) and multiple-trait analyses identified one novel locus (FRMD3) for blood pressure. At these three loci, as well as at GRP20/CDH17, associated variants had alleles common only in African-ancestry populations. Functional annotation showed enrichment for genes expressed in immune and kidney cells, as well as in heart and vascular cells/tissues. Experiments driven by these findings and using angiotensin-II induced hypertension in mice showed altered kidney mRNA expression of six genes, suggesting their potential role in hypertension. Our study provides new evidence for genes related to hypertension susceptibility, and the need to study African-ancestry populations in order to identify biologic factors contributing to hypertension.

A genome-wide association study implicates NR2F2 in lymphangioleiomyomatosis pathogenesis.

INTRODUCTION: Lymphangioleiomyomatosis (LAM) occurs either associated with tuberous sclerosis complex (TSC) or as sporadic disease (S-LAM). Risk factors for development of S-LAM are unknown. We hypothesised that DNA sequence variants outside of TSC2/TSC1 might be associated with susceptibility for S-LAM and performed a genome-wide association study (GWAS). METHODS: Genotyped and imputed data on 5 426 936 single nucleotide polymorphisms (SNPs) in 426 S-LAM subjects were compared, using conditional logistic regression, with similar data from 852 females from COPDGene in a matched case-control design. For replication studies, genotypes for 196 non-Hispanic White female S-LAM subjects were compared with three different sets of controls. RNA sequencing and immunohistochemistry analyses were also performed. RESULTS: Two noncoding genotyped SNPs met genome-wide significance: rs4544201 and rs2006950 (p=4.2×10-8 and 6.1×10-9, respectively), which are in the same 35 kb linkage disequilibrium block on chromosome 15q26.2. This association was replicated in an independent cohort. NR2F2 (nuclear receptor subfamily 2 group F member 2), a nuclear receptor and transcription factor, was the only nearby protein-coding gene. NR2F2 expression was higher by RNA sequencing in one abdominal LAM tumour and four kidney angiomyolipomas, a LAM-related tumour, compared with all cancers from The Cancer Genome Atlas. Immunohistochemistry showed strong nuclear expression in both LAM and angiomyolipoma tumours. CONCLUSIONS: SNPs on chromosome 15q26.2 are associated with S-LAM, and chromatin and expression data suggest that this association may occur through effects on NR2F2 expression, which potentially plays an important role in S-LAM development.

Selecting cases and controls for DNA sequencing studies using family histories of disease.

Recent improvements in sequencing technology have enabled the investigation of so-called missing heritability, and a large number of affected subjects have been sequenced in order to detect significant associations between human diseases and rare variants. However, the cost of genome sequencing is still high, and a statistically powerful strategy for selecting informative subjects would be useful. Therefore, in this report, we propose a new statistical method for selecting cases and controls for sequencing studies based on family history. We assume that disease status is determined by unobserved liability scores. Our method consists of two steps: first, the conditional means of liability are estimated with the liability threshold model given the individual's disease status and those of their relatives. Second, the informative subjects are selected with the estimated conditional means. Our simulation studies showed that statistical power is substantially affected by the subject selection strategy chosen, and power is maximized when affected (unaffected) subjects with high (low) risks are selected as cases (controls). The proposed method was successfully applied to genome-wide association studies for type 2 diabetes, and our analysis results reveal the practical value of the proposed methods. Copyright © 2017 John Wiley & Sons, Ltd.

Family-based association analysis: a fast and efficient method of multivariate association analysis with multiple variants.

BACKGROUND: Many disease phenotypes are outcomes of the complicated interplay between multiple genes, and multiple phenotypes are affected by a single or multiple genotypes. Therefore, joint analysis of multiple phenotypes and multiple markers has been considered as an efficient strategy for genome-wide association analysis, and in this work we propose an omnibus family-based association test for the joint analysis of multiple genotypes and multiple phenotypes. RESULTS: The proposed test can be applied for both quantitative and dichotomous phenotypes, and it is robust under the presence of population substructure, as long as large-scale genomic data is available. Using simulated data, we showed that our method is statistically more efficient than the existing methods, and the practical relevance is illustrated by application of the approach to obesity-related phenotypes. CONCLUSIONS: The proposed method may be more statistically efficient than the existing methods. The application was developed in C++ and is available at the following URL: http://healthstat.snu.ac.kr/software/mfqls/ .

Comparison of RNA-Seq and microarray in the prediction of protein expression and survival prediction.

Gene expression profiling using RNA-sequencing (RNA-seq) and microarray technologies is widely used in cancer research to identify biomarkers for clinical endpoint prediction. We compared the performance of these two methods in predicting protein expression and clinical endpoints using The Cancer Genome Atlas (TCGA) datasets of lung cancer, colorectal cancer, renal cancer, breast cancer, endometrial cancer, and ovarian cancer. We calculated the correlation coefficients between gene expression measured by RNA-seq or microarray and protein expression measured by reverse phase protein array (RPPA). In addition, after selecting the top 103 survival-related genes, we compared the random forest survival prediction model performance across test platforms and cancer types. Both RNA-seq and microarray data were retrieved from TCGA dataset. Most genes showed similar correlation coefficients between RNA-seq and microarray, but 16 genes exhibited significant differences between the two methods. The BAX gene was recurrently found in colorectal cancer, renal cancer, and ovarian cancer, and the PIK3CA gene belonged to renal cancer and breast cancer. Furthermore, the survival prediction model using microarray was better than the RNA-seq model in colorectal cancer, renal cancer, and lung cancer, but the RNA-seq model was better in ovarian and endometrial cancer. Our results showed good correlation between mRNA levels and protein measured by RPPA. While RNA-seq and microarray performance were similar, some genes showed differences, and further clinical significance should be evaluated. Additionally, our survival prediction model results were controversial.

Initial experience with the enhanced recovery after surgery (ERAS) protocols in gynecologic surgery at an urban academic tertiary medical center.

Background: The enhanced recovery after surgery (ERAS) protocols have been consistently associated with improved patient experience and surgical outcomes. Despite the release of ERAS Society guidelines specific to gynecologic oncology, the adoption of ERAS in gynecology on global level has been disappointingly low and some centers have shown minimal improvement in clinical outcomes after adopting ERAS. The aim of this study is to describe the development and early experience of ERAS protocols in gynecologic surgery at an urban academic tertiary medical center. Methods: This was an observational prospective cohort study. The target patient population included those with low comorbidities who were scheduled to undergo various types of gynecologic surgeries for both benign and malignant diseases between October 2020 and February 2021. Two attending surgeons implemented the protocols for their patients (ERAS cohort) while three attending surgeons maintained the conventional perioperative care for their patients (non-ERAS cohort). Baseline characteristics, surgical outcomes and patients' answers to a 12-question survey were compared. A case-matched comparative analysis was also performed between the ERAS cohort and the historical non-ERAS cohort (those who received the same types of surgical procedures from the two ERAS attending surgeons prior to the implementation of the protocols). Results: A total of 244 patients were evaluated (122 in the ERAS cohort vs. 122 in the non-ERAS cohort). The number of vials of opioid analgesia used during the first two postoperative days was significantly lower whereas the use of nonsteroidal anti-inflammatory drugs (NSAIDs) and acetaminophen was more frequent in the ERAS cohort group. The patients in the ERAS group reported less postoperative pain, feelings of hunger and thirst, and greater amount of exercise postoperatively. These benefits of the ERAS cohort were more pronounced in the patients who underwent laparotomic surgeries than those who underwent laparoscopic surgeries. The case-matched comparative analysis also showed similar results. The length of hospital stay did not differ between those who underwent the ERAS protocols and those who did not. Conclusions: The results of the study demonstrated the safety, clinical feasibility and benefits of the ERAS protocols for patients undergoing gynecologic surgeries for both benign and malignant indications.

Machine learning models for predicting blood pressure phenotypes by combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1 to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8 to 5.1% (SBP) and 4.7 to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs. In summary, non-linear ML models improves BP prediction in models incorporating diverse populations.

Machine learning models for blood pressure phenotypes combining multiple polygenic risk scores.

We construct non-linear machine learning (ML) prediction models for systolic and diastolic blood pressure (SBP, DBP) using demographic and clinical variables and polygenic risk scores (PRSs). We developed a two-model ensemble, consisting of a baseline model, where prediction is based on demographic and clinical variables only, and a genetic model, where we also include PRSs. We evaluate the use of a linear versus a non-linear model at both the baseline and the genetic model levels and assess the improvement in performance when incorporating multiple PRSs. We report the ensemble model's performance as percentage variance explained (PVE) on a held-out test dataset. A non-linear baseline model improved the PVEs from 28.1% to 30.1% (SBP) and 14.3% to 17.4% (DBP) compared with a linear baseline model. Including seven PRSs in the genetic model computed based on the largest available GWAS of SBP/DBP improved the genetic model PVE from 4.8% to 5.1% (SBP) and 4.7% to 5% (DBP) compared to using a single PRS. Adding additional 14 PRSs computed based on two independent GWASs further increased the genetic model PVE to 6.3% (SBP) and 5.7% (DBP). PVE differed across self-reported race/ethnicity groups, with primarily all non-White groups benefitting from the inclusion of additional PRSs.

Structural variation across 138,134 samples in the TOPMed consortium.

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.