Both short and long sleep duration have been associated with poor glycemic control and an increased risk of developing type 2 diabetes mellitus. Although sleep duration may differentially modify the effects of genetic risk factors for type 2 diabetes, this has not been systematically investigated. In the present study, we conducted genome-wide gene by sleep duration meta-analyses, separately assessing interactions of short and long sleep, for fasting glucose, fasting insulin, and hemoglobin A1c in up to 489,309 individuals without diabetes from seven different population groups. In total, 16 loci were identified to interact with sleep duration - six with short sleep and ten with long sleep. Of these, four loci were identified through cross-population meta-analysis. Mapped genes exhibit pathway connections to pericyte apoptosis, NMDA receptor activity, the GLUT1 receptor, neurological health, and sleep architecture. Eleven loci (VRK2, PCDH7, TFAP2A, CAP2, PAPPA, ZCCHC2, MYH9, SGIP1, JAKMIP3, RRAS2, MAPT) have not been reported in previous glycemic trait genome-wide association studies. Interaction loci identify divergent biological mechanisms for short and long sleep duration influencing glycemic control, suggesting specific pathways of intervention for precision medicine approaches to diabetes prevention and management.

STUDY OBJECTIVES:Excessive daytime sleepiness (EDS), influenced by environmental and social-behavioral factors, is reported by a subset of patients with sleep apnea-a group that may be at elevated cardiovascular risk. However, it is unclear whether sleep apnea with and without EDS have distinct genetic underpinnings. In this study, we perform gene-by-EDS interaction analyses for apnea hypopnea index, a diagnostic marker of sleep apnea severity, to understand EDS's influence on its underlying genetic risk. METHODS:Discovery interaction analyses for common variants and gene-based rare variants were conducted respectively using multi-ethnic Trans-Omics for Precision Medicine (N = 11 619) data, followed by replication and subsequent meta-analysis in additional Trans-Omics for Precision Medicine-imputed data (N = 8904). The 1 degree-of-freedom (1df) G × E test and the 2df joint G,G × E tests were utilized. Sex-stratified analyses were additionally performed. RESULTS:Discovery analysis revealed two common intronic variants-rs13118183 (CCDC3) and rs281851 (MARCHF1)-and three rare variant gene sets mapped to SCUBE2, TMEM26, and CPS4FL-to exhibit interaction with EDS. Meta-analysis revealed EDS interaction with 11 rare variant gene sets mapped to UBLCP1, MED31, RAP1GAP, CPNE5, MYMX, YY1, ZNF773, YBEY, IQCB1, PI4K2B, and CORO1A. CONCLUSION:Genetic loci reveal connections to cardiovascular risk, insulin resistance, thiamine deficiency, and resveratrol mechanism. Discovered genetic signals may offer insight into pertinent biological pathways for sleep apnea patients with an excessively sleepy subtype. Statement of Significance Sleep apnea is a complex sleep disorder. Exemplifying this is the disparately varying estimates of presence of excessive daytime sleepiness (EDS) in patients, and persistent EDS that lingers despite treatment. Some data indicate that the excessively sleepy subtype of sleep apnea carries heightened cardiovascular risk. Whether EDS influences genetic risk factors underlying sleep apnea has not yet been investigated. This study addresses this gap, as the first genome-wide gene × EDS interaction study for apnea hypopnea index, the standard sleep apnea severity metric. Genetic loci that have been previously unconsidered for sleep apnea are revealed. Discovered interaction signals highlight pathways in metabolism, genes associated with cardiometabolic traits, and therapeutic agents influencing obesity, blood pressure, oxidative stress, and apnea hypopnea index.

Fatigue, distinct from sleepiness, is one of the most common obstructive sleep apnea (OSA) symptoms, and is associated with diminished quality of life, poor psychological and physical functioning and increased risk of occupational and motor vehicle accidents. The aim of this study was to estimate the incremental healthcare cost burden of fatigue among patients with OSA in the Medicare fee-for-service (FFS) population. Retrospective observational claims analysis was conducted comparing newly diagnosed OSA patients with fatigue to matched OSA patients without fatigue. Medical and pharmacy claims from the 2017-2022 Medicare FFS database were used to identify incident cases of fatigue among newly diagnosed OSA patients who met the following criteria: ≥1 inpatient or ≥2 outpatient claims (with ≥7 days apart) with an ICD-10-CM diagnosis code of OSA (G47.33, G47.30, G47.39); ≥1 procedure code for polysomnography or home sleep apnea test in 12 months prior to OSA diagnosis index date; continuous insurance coverage ≥12 months before OSA diagnosis index (baseline period) and ≥12 months after fatigue index date (follow-up period). Fatigue cases had ≥1 claim for fatigue (R53.1, R53.81, R53.82, R53.83) after the OSA diagnosis index date. Controls had no diagnosis of fatigue and were propensity score matched to fatigue cases. Total of 71,710 newly diagnosed OSA patients met eligibility criteria, of whom ~36% had ≥1 diagnosis claim for fatigue. Mean (SD) age was 72.8 years (5.3), 51% male, and 88% White. After OSA diagnosis, all-cause healthcare costs were significantly higher (all p< 0.001) for patients with fatigue vs. matched controls: Per-patient-per-year (PPPY) all-cause hospitalization ($7,721 vs $1,998); outpatient visits ($5,225 vs $3,004); emergency department visits ($882 vs $291); other medical visits ($11,005 vs $5,455); and pharmacy costs ($5,192 vs $3,643). Overall, the PPPY all-cause total costs ($30,025 vs $14,390, p< 0.001) in OSA patients with fatigue were significantly higher than those without fatigue. Fatigue is significantly associated with an increase in all-cause total healthcare costs in newly diagnosed OSA patients as compared with matched patients without fatigue. Addressing fatigue may be useful as part of OSA screening, diagnosis and treatment due to the incremental humanistic and economic burden. Apnimed Inc.

RATIONALE:Advanced polysomnographic (PSG) metrics reflecting the physiological causes and consequences of sleep apnea may enable precision medicine in research settings, but their feasibility in routine clinical practice has yet to be demonstrated. OBJECTIVE:Assess (1) the generalizability of PSG metrics from research to clinical cohort, and (2) their associations with a broad range of comorbid diseases, many of which have not been previously examined. METHODS:PSG metrics including endotypes (eg, loop gain) and physiological burdens (eg, hypoxic burden) were estimated from diagnostic polysomnographs of 6,427 participants at Mass General Brigham (MGB; Boston, MA). Comorbid conditions analyzed from MGB's medical record system included 9 representative cardio-metabolic and respiratory diseases, as well as 408 prevalent diseases. Associations were assessed using modified Poisson and LASSO regression. RESULTS:The sample included 62% females, age: 52.9 ± 16.8 years, and apnea-hypopnea index (AHI) 21.4 ± 15.9 events/hr. Associations between endotypes and demographics/obesity-related factors were consistent with prior observational studies (median difference in β = 0.03SD). After adjusting for AHI, older age was associated with lower heart rate (-0.40SD) and arousal burdens (-0.23SD), while higher BMI was associated with increased hypoxic burden (0.25SD). Having demonstrated that there is reasonable concordance with published data, our subsequent analysis identified distinct and clinically meaningful associations between advanced PSG metrics and comorbid conditions. Specifically, elevated loop gain, ventilatory burden, and hypoxic burden were associated with hypertension, diabetes, and renal failure; increased ventilatory instability was associated with cardiovascular disease; and reduced collapsibility and ventilatory instability with chronic airway obstruction. Even after LASSO-based selection, no single PSG metric consistently predicted risk across all comorbidities; ventilatory instability showed the most associations among endotypic traits, and heart rate burden among physiological burdens, underscoring the heterogeneity of OSA pathophysiology. CONCLUSIONS:Phenome-wide analyses of a large clinical cohort demonstrate the real-world feasibility and clinical relevance of extracting advanced PSG metrics, supporting their potential to identify personalized, mechanism-specific intervention targets for sleep apnea.

Large-scale multiancestry genome-wide association studies have identified hundreds of loci associated with type 2 diabetes (T2D) and glycemic traits, yet imputed genotyping arrays limit the detection of low-frequency and rare variants. Whole-genome sequencing (WGS) offers a more complete view of genetic variation, especially across diverse populations. We analyzed high-coverage (38×) WGS data from 21,913 T2D case subjects, 61,036 control subjects, and up to 50,011 individuals with no diabetes with fasting glucose, fasting insulin, and HbA1c from the National Heart, Lung, and Blood Institute Trans-Omics for Precision Medicine Program. We performed single-variant association testing, conditional analysis, fine-mapping, and Bayesian colocalization to identify genetic signals and assess regulatory relevance in diabetes-related tissues. We identified 76 distinct association signals across 34 loci, including novel variants at DUSP9 for T2D, and ROBO1, NDN, and MYT1 for HbA1c. Fine-mapping narrowed credible sets and improved causal variant resolution. Colocalization highlighted 80 expression signals in diabetes-related tissues, linking genetic associations to functional regulatory mechanisms. Our findings demonstrate the utility of WGS to uncover novel variants in diverse populations, enhance locus resolution, and link regulatory variation to disease-relevant tissues. This work refines the genetic architecture of T2D and glycemic traits and supports precision medicine efforts targeting diverse populations. ARTICLE HIGHLIGHTS:We aimed to improve understanding of the genetic architecture of type 2 diabetes and glycemic traits by leveraging whole-genome sequencing in diverse populations. Our goal was to identify novel variants, refine known loci, and link genetic signals to regulatory mechanisms through colocalization with expression quantitative trait loci. We discovered novel variants, significantly improved fine-mapping resolution, and identified 80 regulatory colocalization signals in diabetes-relevant tissues. These findings support precision medicine approaches by connecting genetic variation to functional biology in type 2 diabetes.

Obstructive sleep apnea (OSA) is associated with a wide range of comorbidities, but the extent to which these follow predictable, age-dependent patterns is not well understood. Identifying such patterns could provide insight into OSA heterogeneity and its links to physiological measures of OSA. We trained age-dependent topic models (ATM) on longitudinal electronic health records from 36,426 patients with OSA in the Mass General Brigham Biobank. ATM organizes incident diagnoses into distinct comorbidity "topics," whose age-specific disease loadings represent predictive patterns linking related diagnoses across the life course. We applied the trained model to compute individual-level topic scores in independent data: a cohort of 11,689 OSA cases and 22,695 matched controls, and a cohort of 6,220 patients with polysomnography (PSG)-derived physiological measures. We identified 19 distinct age-dependent comorbidity profiles, all significantly associated with OSA case status (FDR-adjusted p<0.05). Topics reflected recognizable clusters including metabolic, neuropsychiatric, and immune-mediated conditions, and several were distinguished by age-of-onset of key comorbidities, such as early- vs late-onset asthma. Seventeen of the 19 topics were significantly associated with at least one of 13 PSG-derived physiological measures, including associations between cardiometabolic topics and the apnea-hypopnea index, sleep apnea specific hypoxic burden, and respiratory event-specific heart rate burden. These findings indicate that age-dependent comorbidity patterns distinguish meaningful OSA subtypes with differing prognoses and endophenotype associations. ATM offers insight into complex OSA comorbidity and suggests that age-informed, topic-based stratification may improve individualized risk assessment, interpretation of PSG findings, and targeting of clinical interventions. One Sentence Summary:Analysis of age-specific patterns in comorbidities of obstructive sleep apnea reveals insights for personalized care and risk stratification.

Background Genetics and environmental factors contribute to obesity risk, but the extent to which healthy behaviors can offset genetic susceptibility remains unclear. We examined the interaction between obesity polygenic risk and a composite healthy lifestyle score on body mass index (BMI) trajectories in women and men. Methods We analyzed 13 780 women from the Nurses' Health Study and 8242 men from the Health Professionals Follow‐Up Study, all of European ancestry and free of major chronic disease at baseline. The lifestyle score comprised American Heart Association Essential 8 components (nonsmoking, physical activity, healthy eating, adequate sleep) plus moderate alcohol intake, modeled as a time‐varying variable. A genome‐wide polygenic score for BMI was derived from genome‐wide association study. Adjusted linear mixed‐effects models estimated associations and interactions on biennial BMI measures over up to 26 years. Results Each SD increase in the polygenic score was associated with 1.80 kg/m2 (95% CI, 1.72–1.87) and 1.12 kg/m2 (95% CI, 1.06–1.19) higher BMI in women and men, respectively. Significant interactions between the polygenic score and healthy lifestyle score (both P<0.05) showed a dose–response attenuation of the genetic effects with healthier lifestyles. Comparing the healthiest with the least healthy lifestyle groups, genetic effects on BMI were 35% lower in women and 28% lower in men. In sensitivity analyses, higher diet quality and physical activity consistently attenuated genetic associations in both cohorts, whereas current smoking showed similar effects in women only. Conclusions Adherence to a healthier lifestyle attenuated the association between obesity polygenic risk and BMI in a dose–response manner.

Whole genome sequence (WGS) data in multi-ancestry samples supports discovery of low-frequency or population-specific genetic variants associated with chronic obstructive pulmonary disease (COPD) and lung function. We performed single variant, structural variant, and gene-based analysis of pulmonary function (FEV1, FVC and FEV1/FVC) and COPD case–control status in 44,287 multi-ancestry participants from the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program. We validated findings using the UK Biobank and assessed implicated genes using lung single-cell RNA-seq (scRNA-seq) data sets. Applying a genome-wide significance threshold (P < 5 × 10–9), we replicated known loci and identified novel associations near LY86, MAGI1, GRK7, and LINC02668. Colocalization with gene expression quantitative trait loci (eQTL) from the Lung Tissue Research Consortium highlighted known candidate genes including ADAM19, THSD4, C4B, and PSMA4, which were not identified through other eQTL sources. Multi-ancestry analysis improved fine-mapping resolution (e.g., HTR4 and RIN3). Gene-based analysis identified and replicated HMCN1. In human lung scRNA-seq data sets, lung epithelial cells and immune cell types showed enriched expression, while fibroblasts showed higher expression for HMCN1. CRISPR targeting HMCN1 in IMR90 demonstrated reduced expression of collagen genes. Large-scale multi-ancestry WGS analysis improves variant discovery and fine-mapping resolution for lung function and COPD and highlights biologically relevant genes and pathways.

BACKGROUND:Obstructive sleep apnoea (OSA) is a common chronic condition, with obesity its strongest risk factor. Polygenic scores (PGSs) summarise the genetic liability to phenotype and can provide insights into relationships between phenotypes. Recently, large datasets that include genetic data and OSA status became available, providing an opportunity to utilise PGS approaches to study the genetic relationship between OSA and other phenotypes, while differentiating OSA-specific from obesity-specific genetic factors. METHODS:Using race/ethnic diverse samples from over 1.2 million individuals from the Million Veteran Program, FinnGen, TOPMed, All of Us (AoU), Geisinger's MyCode, MGB Biobank, and the Human Phenotype Project, we developed and assessed PGSs for OSA, both without (BMIunadjOSA-PGS) and with adjustment for the genetic contributions of BMI (BMIadjOSA-PGS). FINDINGS:Adjusted odds ratios (ORs) for OSA per 1 standard deviation of the PGSs ranged from 1.38 to 2.75. The associations of BMIadjOSA- and BMIunadjOSA-PGSs with CVD outcomes in AoU shared both common and distinct patterns. Only BMIunadjOSA-PGS was associated with type 2 diabetes, heart failure, and coronary artery disease, while both BMIadjOSA- and BMIunadjOSA-PGSs were associated with hypertension and stroke. Sex stratified analyses revealed that BMIadjOSA-PGS association with hypertension was driven by females (OR = 1.1, p-value = 0.002, OR = 1.01 p-value = 0.2 in males). OSA PGSs were also associated with body fat measures with some sex-specific associations. INTERPRETATION:Distinct components of OSA genetic risk are related and independent of obesity. Sex-specific associations with body fat distribution measures may explain differing OSA risks and associations with cardiometabolic morbidities between sexes. FUNDING:R01AG080598.

Background:Obstructive sleep apnea (OSA) is associated with a wide range of comorbidities, but large-scale phenome-wide analyses in clinical biobanks remain under-reported. In this study, we identified common comorbidities enriched in patients with OSA, tested the temporality of these associations, and analyzed relevant associations with summary sleep recording data. Methods:48,251 participants with OSA in the Mass General Brigham healthcare system were identified using a natural language processing phenotyping algorithm and/or evidence of an elevated apnea-hypopnea index (AHI). Controls were matched (2:1) on demographics, body mass index (BMI), and healthcare utilization. Associations with 358 incident and 563 cross-sectional diseases were tested using Modified Poisson regression, adjusting for covariates. Sensitivity analyses examined timing by binning data in years relative to the first OSA diagnosis. Selected laboratory results were obtained based on associated diseases. Associated diseases were tested with sleep recording statistics (n ≤18,348). Findings:179 incident and 421 cross-sectional diseases were associated with OSA at Bonferroni significance. 37 diseases had Bonferroni-significant sex interactions. Several associations were significant years before the first recorded OSA diagnosis. Four red blood cell laboratory measures were significant ten years prior to the first diagnosis. One incident and 47 cross-sectional diseases were associated with the AHI and/or chronic hypoxemia. Interpretation:Obstructive sleep apnea is associated with enrichment of hundreds of diseases, several of which are supported by orthogonal polysomnographic evidence. Leveraging early signs of OSA in clinical data may help to identify at-risk patients.

Whole genome sequencing (WGS) studies have identified hundreds of millions of rare variants (RVs) and have enabled RV association tests (RVATs) of these variants with complex traits and diseases. Analysis of non-coding variants is challenged by the considerable variability in regulatory function which candidate Cis-Regulatory Elements (cCREs) exhibit across cell types. We propose cellSTAAR, which integrates WGS data with single-cell ATAC-seq data to capture variability in chromatin accessibility across cell types via the construction of cell-type-specific functional annotations and variant sets. To reflect the uncertainty in cCRE-gene linking, cellSTAAR also links cCREs to their target genes using an omnibus framework which aggregates results from a variety of popular linking approaches. We applied cellSTAAR on Freeze 8 (N = 60,000) of the NHLBI Trans-Omics for Precision Medicine (TOPMed) consortium data to four lipids phenotypes: LDL cholesterol, a binary variable corresponding to high LDL cholesterol, HDL cholesterol, and triglycerides. We also provide replication results for all four phenotypes using UK Biobank (N = 190,000). Evidence from simulation studies and our real data analysis demonstrates that cellSTAAR boosts power and improves interpretation of RVATs of cCREs. ### Competing Interest Statement The authors have declared no competing interest. National Institutes of Health, ,

In studies of individuals of primarily European genetic ancestry, common and low-frequency variants and rare coding variants have been found to be associated with the risk of bipolar disorder (BD) and schizophrenia (SZ). However, less is known for individuals of other genetic ancestries or the role of rare non-coding variants in BD and SZ risk. We performed whole-genome sequencing (∼27X) of African American individuals: 1,598 with BD, 3,295 with SZ, and 2,651 unaffected controls (InPSYght study). We increased power by incorporating 14,812 jointly called psychiatrically unscreened ancestry-matched controls from the Trans-Omics for Precision Medicine (TOPMed) Program for a total of 17,463 controls (∼37X). To identify variants and sets of variants associated with BD and/or SZ, we performed single-variant tests, gene-based tests for singleton protein truncating variants, and rare and low-frequency variant annotation-based tests with conservation and universal chromatin states and sliding windows. We found suggestive evidence of the association of BD with single variants on chromosome 18 and of lower BD risk associated with rare and low-frequency variants on chromosome 11 in a region with multiple BD genome-wide association study loci, using a sliding window approach. We also found that chromatin and conservation state tests can be used to detect differential calling of variants in controls sequenced at different centers and to assess the effectiveness of sequencing metric covariate adjustments. Our findings reinforce the need for continued whole-genome sequencing in additional samples of African American individuals and more comprehensive functional annotation of non-coding variants.

Polygenic scores (PGSs) for body mass index (BMI) may guide early prevention and targeted treatment of obesity. Using genetic data from up to 5.1 million people (4.6% African ancestry, 14.4% American ancestry, 8.4% East Asian ancestry, 71.1% European ancestry and 1.5% South Asian ancestry) from the GIANT consortium and 23andMe, Inc., we developed ancestry-specific and multi-ancestry PGSs. The multi-ancestry score explained 17.6% of BMI variation among UK Biobank participants of European ancestry. For other populations, this ranged from 16% in East Asian-Americans to 2.2% in rural Ugandans. In the ALSPAC study, children with higher PGSs showed accelerated BMI gain from age 2.5 years to adolescence, with earlier adiposity rebound. Adding the PGS to predictors available at birth nearly doubled explained variance for BMI from age 5 onward (for example, from 11% to 21% at age 8). Up to age 5, adding the PGS to early-life BMI improved prediction of BMI at age 18 (for example, from 22% to 35% at age 5). Higher PGSs were associated with greater adult weight gain. In intensive lifestyle intervention trials, individuals with higher PGSs lost modestly more weight in the first year (0.55 kg per s.d.) but were more likely to regain it. Overall, these data show that PGSs have the potential to improve obesity prediction, particularly when implemented early in life.

Obstructive sleep apnea (OSA) is a prevalent disorder associated with numerous comorbidities, including cardiometabolic and neuropsychiatric diseases. Heterogeneity in presentation and multi-morbidity complicates disease management. This analysis adopts a validated data-driven approach, age-dependent topic modeling (ATM; PMID 37814053), to group OSA-related diagnoses into longitudinal trajectories, elucidating comorbidity progression and its relationship with OSA. We analyzed longitudinal electronic health records (EHR) from 38,600 OSA patients in the Mass General Brigham (MGB) system using 411 diagnoses previously associated with OSA. OSA status was ascertained with a validated algorithm incorporating EHR and available polysomnography (PSG). ATM analysis then grouped incident EHR diagnoses into age-dependent patterns (topics) optimized to predict future diagnoses from any given age. Model weights were used to calculate individualized risk scores for each topic, which were standardized and tested for association with OSA in a separate validation cohort of 7,774 OSA cases and 15,294 controls, matched 2:1 on propensity score (accounting for age, sex, BMI, ancestry, and healthcare utilization), and further adjusted for residual confounding. ATM identified 17 distinct age-dependent comorbidity topics in OSA patients. Of these, 14 were significantly associated (p< 0.05/17) with higher scores in OSA cases, representing greater risk for diverse conditions including neuropsychiatric, cardiometabolic, and respiratory disorders. The strongest associations were observed for: 1. A neuropsychiatric topic (β=0.43, p< 1.88e-222), progressing from mood disorders (early-adulthood) to insomnia (middle-age) and cognitive decline (old-age). 2. A respiratory-cardiovascular topic (β=0.35, p< 1.35e-147), progressing from chronic respiratory infections (childhood) to hearing loss and hypertension (middle-age), and cardio-vascular disease (old-age). For example, a 60-year-old with prior mood disorders and incident insomnia is expected to score high in the neuropsychiatric topic, a scenario observed more frequently in OSA, suggesting risk of later cognitive decline. This study highlights associations between OSA and distinct multimorbidity patterns and suggests age-dependent comorbidity progression as a framework for understanding comorbidity burden in OSA. These findings have potential to guide targeted management strategies and interventions. Future work will integrate longitudinal data and OSA endotypes to further investigate disease heterogeneity and explore physiologically relevant pathways. This study was supported by NIH R01HL153805 and the AASM Foundation 338-SR-24.

Understanding how rare genetic variants influence complex traits remains a major challenge, particularly when these variants lie in noncoding regions of the genome. The effects of variants within candidate cis-regulatory elements (cCREs) often depend on the cell type, making interpretation difficult. Here we introduce cellSTAAR, which integrates whole-genome sequencing data with single-cell assay for transposase-accessible chromatin using sequencing data to capture variability in chromatin accessibility across cell types via the construction of cell-type-specific functional annotations and regulatory elements. To reflect the uncertainty in cCRE-gene linking, cellSTAAR uses a comprehensive strategy to link cCREs to their target genes. We applied cellSTAAR to data from the Trans-Omics for Precision Medicine consortium (n ≈ 60,000) and replicated our findings using the UK Biobank (n ≈ 190,000). Across four lipid traits, cellSTAAR improved the detection of biologically meaningful associations and enhanced biological interpretability. These results demonstrate the potential of cell-type-aware approaches to boost discovery in rare variant whole-genome sequencing association studies.

Rare genetic variation provided by whole genome sequence datasets has been relatively less explored for its contributions to human traits. Meta-analysis of sequencing data offers advantages by integrating larger sample sizes from diverse cohorts, thereby increasing the likelihood of discovering novel insights into complex traits. Furthermore, emerging methods in genome-wide rare variant association testing further improve power and interpretability. Here, we conduct the largest meta-analysis of whole genome sequencing for low-density lipoprotein cholesterol (LDL-C), a therapeutic target for coronary artery disease, analyzing data from 246 K participants and integrating 1.23B variants from the UK Biobank and the Trans-Omics for Precision Medicine (TOPMed) program. We identify numerous rare coding and non-coding gene associations related to LDL-C, with replication across 86 K participants in All of Us. Our findings are based on single-variant analyses, rare coding and non-coding variant aggregation tests, and sliding window approaches. Through this comprehensive analysis, we identify 704 novel single-variant associations, 25 novel rare coding variant aggregates, 28 novel rare non-coding variant aggregates, and one novel sliding window aggregate. This study provides a meta-analysis framework for large-scale whole genome sequence association analyses from diverse population groups, yielding novel rare non-coding variant associations.

Background: OSA is a heterogeneous disease, with obesity a significant risk factor in many but not all cases of OSA, via increased airway collapsibility, reduced lung volumes, and possibly body fat distribution. Research question: We sought to develop PRSs that summarize the genetic liability to OSA that include and exclude obesity related pathways, and to study the associations of these PRSs with OSA comorbid cardiometabolic and CVD outcomes. Approach: Using 1.2 million race/ethnic diverse samples from the Million Veteran Program, FinnGen, TOPMed, All of Us (AoU), Geisinger’s MyCode, MGB Biobank, and the Human Phenotype Project (HPP), we developed, selected, and assessed PRSs for OSA, relying on genome wide association studies both adjusted and unadjusted for BMI: BMIadjOSA and BMIunadjOSA PRS. We tested their associations with cardiometabolic and CVD outcomes in AoU. Results: In association with OSA, adjusted odds ratios (ORs) per 1 standard deviation of the PRSs ranged from 1.38 to 2.75, all statistically significant (Figure). The associations of BMIadjOSA and BMIunadjOSA PRSs with CVD outcomes in AoU shared both common and distinct patterns. For example, BMIunadjOSA PRS was associated with type 2 diabetes, heart failure, and coronary artery disease, but the associations of BMIadjOSA PRS with these outcomes were statistically insignificant with estimated OR close to 1. In contrast, both BMIadjOSA and BMIunadjOSA PRSs were associated with hypertension and stroke. Sex stratified analyses revealed that BMIadjOSA PRS association with hypertension was driven by data from females: females had OR=1.1, p-value=0.002, but males OR=1.01 and statistically insignificant. OSA PRSs were also associated with dual-energy X-ray absorptiometry (DXA) body fat measures. In BMI adjusted analysis, BMIadjOSA PRS was associated with higher visceral adipose tissue (VAT) proportion of total body fat mass (TFM), with lower proportion of gynoid fat mass out of TFM, higher proportion of android fat mass out of TFM, and lower gynoid to android fat mass. In females only, the PRS was associated with higher VAT to SAT ratio (Figure). Conclusions: Distinct components of OSA genetic risk are related to obesity and body fat distribution, and may influence clinical outcomes. These may explain differing OSA risk and associations with cardiometabolic and CVD morbidities between sex groups.

Although both short and long sleep duration are associated with elevated hypertension risk, our understanding of their interplay with biological pathways governing blood pressure remains limited. To address this, we carried out genome-wide cross-population gene-by-short-sleep and long-sleep duration interaction analyses for three blood pressure traits (systolic, diastolic, and pulse pressure) in 811,405 individuals from diverse population groups. We discovered 22 novel gene-sleep duration interaction loci for blood pressure, mapped to 23 genes. Investigating these genes’ functional implications shed light on neurological, thyroidal, bone metabolism, and hematopoietic pathways that necessitate future investigation for blood pressure management that caters to sleep health lifestyle. Non-overlap between short sleep (12) and long sleep (10) interactions underscores the plausible nature of distinct influences of both sleep duration extremes in cardiovascular health. Several of our loci are specific towards a particular population background or sex, emphasizing the importance of addressing heterogeneity entangled in gene-environment interactions, when considering precision medicine design approaches for blood pressure management.

Large-scale whole-genome sequencing (WGS) studies have improved our understanding of the contributions of coding and noncoding rare variants to complex human traits. Leveraging association effect sizes across multiple traits in WGS rare variant association analysis can improve statistical power over single-trait analysis, and also detect pleiotropic genes and regions. Existing multi-trait methods have limited ability to perform rare variant analysis of large-scale WGS data. We propose MultiSTAAR, a statistical framework and computationally-scalable analytical pipeline for functionally-informed multi-trait rare variant analysis in large-scale WGS studies. MultiSTAAR accounts for relatedness, population structure and correlation among phenotypes by jointly analyzing multiple traits, and further empowers rare variant association analysis by incorporating multiple functional annotations. We applied MultiSTAAR to jointly analyze three lipid traits (low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides) in 61,861 multi-ethnic samples from the Trans-Omics for Precision Medicine (TOPMed) Program. We discovered new associations with lipid traits missed by single-trait analysis, including rare variants within an enhancer of NIPSNAP3A and an intergenic region on chromosome 1.