Publications

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.

BACKGROUND: Irregular sleep duration may disrupt circadian rhythms and contribute to metabolic, behavioral, and mood changes, potentially increasing the risk for obesity. However, quantitative data on the relationship between sleep duration irregularity and weight change are lacking.

METHODS: In this prospective study, we analyzed data from 10,572 participants (mean age: 63 years) in the UK Biobank who wore accelerometers for a week between 2013 and 2015 and had two body mass index (BMI; kg/m²) measurements on average 2.5 years apart. Irregular sleep duration was assessed by the within-person standard deviation (SD) of 7-night accelerometer-measured sleep duration.

RESULTS: Participants with sleep duration SD > 60 min versus ≤30 min had 0.24 kg/m2 (95% CI: 0.08, 0.40) higher BMI change (kg/m2), standardized to three-year intervals, and 80% (95% CI: 1.28, 2.52) higher risk for incident obesity, after adjusting for sociodemographic factors, shift work, and baseline BMI or follow-up period (p-nonlinearity <0.02 for both). These associations remained consistent after adjusting for lifestyle, comorbidities, and other sleep factors, including sleep duration. Age, sex, baseline BMI, and genetic predisposition to higher BMI (measured with a polygenic risk score) did not appear to modify the association.

CONCLUSIONS: Since irregular sleep duration is common, trials of interventions targeting sleep irregularity might lead to new public health strategies that tackle obesity.

Here, we present a multi-omics study of type 2 diabetes and quantitative blood lipid and lipoprotein traits conducted to date in Hispanic/Latino populations (nmax = 63,184). We conduct a meta-analysis of 16 type 2 diabetes and 19 lipid trait GWAS, identifying 20 genome-wide significant loci for type 2 diabetes, including one novel locus and novel signals at two known loci, based on fine-mapping. We also identify sixty-one genome-wide significant loci across the lipid/lipoprotein traits, including nine novel loci, and novel signals at 19 known loci through fine-mapping. Next, we analyze genetically regulated expression, perform Mendelian randomization, and analyze association with transcriptomic and proteomic measure using multi-omics data from a Hispanic/Latino population. Using this approach, we identify genes linked to type 2 diabetes and lipid/lipoprotein traits, including TMEM205 and NEDD9 for HDL cholesterol, TREH for triglycerides, and ANXA4 for type 2 diabetes.

PURPOSE OF REVIEW: Sleep and circadian biology is fundamental to human health. Following the advancement in sleep medicine and availability of multi-omics technology, this review outlines the current knowledge regarding genetic basis and multi-omics research on circadian rhythm and the two most prevalent sleep disorders, obstructive sleep apnea (OSA) and insomnia.

RECENT FINDINGS: Genome wide association analyses identified variants across circadian genes and genes pertinent to inflammation, obesity and neuronal function associated with OSA and insomnia. Multi-omics integration has led to novel breakthroughs in identifying systemic biomarkers and elucidating cascades, and causal associations underpinning these complex traits.

SUMMARY: Multi-omics studies in sleep and circadian rhythm possess great potential in unveiling molecular mechanisms behind circadian rhythm and sleep, thereby advancing personalized medicine in the long term. Nevertheless, researchers should remain mindful of existing challenges in genetic and multi-omics sleep research, including data harmonization and existing racial and ethnic disparities in data collection and availability, limiting research generalizability.

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.

Here, we present a multi-omics study of type 2 diabetes and quantitative blood lipid and lipoprotein traits conducted to date in Hispanic/Latino populations (nmax = 63,184). We conduct a meta-analysis of 16 type 2 diabetes and 19 lipid trait GWAS, identifying 20 genome-wide significant loci for type 2 diabetes, including one novel locus and novel signals at two known loci, based on fine-mapping. We also identify sixty-one genome-wide significant loci across the lipid/lipoprotein traits, including nine novel loci, and novel signals at 19 known loci through fine-mapping. Next, we analyze genetically regulated expression, perform Mendelian randomization, and analyze association with transcriptomic and proteomic measure using multi-omics data from a Hispanic/Latino population. Using this approach, we identify genes linked to type 2 diabetes and lipid/lipoprotein traits, including TMEM205 and NEDD9 for HDL cholesterol, TREH for triglycerides, and ANXA4 for type 2 diabetes.

PURPOSE OF REVIEW: Sleep and circadian biology is fundamental to human health. Following the advancement in sleep medicine and availability of multi-omics technology, this review outlines the current knowledge regarding genetic basis and multi-omics research on circadian rhythm and the two most prevalent sleep disorders, obstructive sleep apnea (OSA) and insomnia.

RECENT FINDINGS: Genome wide association analyses identified variants across circadian genes and genes pertinent to inflammation, obesity and neuronal function associated with OSA and insomnia. Multi-omics integration has led to novel breakthroughs in identifying systemic biomarkers and elucidating cascades, and causal associations underpinning these complex traits.

SUMMARY: Multi-omics studies in sleep and circadian rhythm possess great potential in unveiling molecular mechanisms behind circadian rhythm and sleep, thereby advancing personalized medicine in the long term. Nevertheless, researchers should remain mindful of existing challenges in genetic and multi-omics sleep research, including data harmonization and existing racial and ethnic disparities in data collection and availability, limiting research generalizability.

In a genome-wide association study (GWAS) meta-analysis of 688,808 individuals with major depression (MD) and 4,364,225 controls from 29 countries across diverse and admixed ancestries, we identify 697 associations at 635 loci, 293 of which are novel. Using fine-mapping and functional tools, we find 308 high-confidence gene associations and enrichment of postsynaptic density and receptor clustering. A neural cell-type enrichment analysis utilizing single-cell data implicates excitatory, inhibitory, and medium spiny neurons and the involvement of amygdala neurons in both mouse and human single-cell analyses. The associations are enriched for antidepressant targets and provide potential repurposing opportunities. Polygenic scores trained using European or multi-ancestry data predicted MD status across all ancestries, explaining up to 5.8% of MD liability variance in Europeans. These findings advance our global understanding of MD and reveal biological targets that may be used to target and develop pharmacotherapies addressing the unmet need for effective treatment.

Recent genome-wide association studies (GWASs) of several individual sleep traits have identified hundreds of genetic loci, suggesting diverse mechanisms. Moreover, sleep traits are moderately correlated, so together may provide a more complete picture of sleep health, while illuminating distinct domains. Here we construct novel sleep health scores (SHSs) incorporating five core self-report measures: sleep duration, insomnia symptoms, chronotype, snoring, and daytime sleepiness, using additive (SHS-ADD) and five principal components-based (SHS-PCs) approaches. GWASs of these six SHSs identify 28 significant novel loci adjusting for multiple testing on six traits (p < 8.3e-9), along with 341 previously reported loci (p < 5e-08). The heritability of the first three SHS-PCs equals or exceeds that of SHS-ADD (SNP-h2 = 0.094), while revealing sleep-domain-specific genetic discoveries. Significant loci enrich in multiple brain tissues and in metabolic and neuronal pathways. Post-GWAS analyses uncover novel genetic mechanisms underlying sleep health and reveal connections (including potential causal links) to behavioral, psychological, and cardiometabolic traits.

Light is an environmental feature important for human physiology. Investigation of how light affects population health requires exposure assessment and personal biomonitoring efforts. Here, we derived measures of amount, duration, regularity, and timing from objective personal light (lux) measurement in >4000 participants across two United States (US)-based cohort studies, the Multi-Ethnic Study of Atherosclerosis (MESA) and the Hispanic Community Health Study / Study of Latinos (HCHS/SOL), encompassing eight geographic regions. Objective light and actigraphy data were collected over a week using wrist-worn devices (Actiwatch Spectrum). Cohort-stratified light exposure metrics were analyzed in relation to sex, season, time-of-day, location, and demographic and sleep health characteristics using Spearman correlation and linear and logistic regressions (separately by cohort) adjusted for age, sex (where applicable), and exam site. Light exposure showed sex-specific patterns and had seasonal, diurnal, geographic, and demographic and sleep health-related correlates. Results between independent cohorts were strongly consistent, supporting the utility and feasibility of light biomonitoring. These findings provide a fundamental first characterization of light exposure patterns in a large US sample and will inform future work to incorporate light as a biologically relevant exposure in environmental public health and key component of the human exposome.