Sleep is an evolutional conserved behavior across species and has a strong genetic basis. In human, sleep duration, timing, quality, and disturbances are influenced by multiple systems including central nervous control, metabolism, and inflammation pathways. To screen for novel genes and pathways for sleep phenotypes, we conduct genome-wide association studies and gene-based rare variant association studies on both self-reported and objective sleep traits (metrices derived from overnight polysomnography and 24-hour actigraphy) in large biobanks and population-based cohorts. Our studies have led to the discovery of multiple novel genetic loci and genes associated with common sleep traits, such as obstructive sleep apnea and excessive daytime sleepiness. To increase statistical power, we also apply linkage analysis leveraging variant enrichment in family data and admixture mapping analysis modeling differential variant frequencies across continental ancestors in admixed populations. Our current research focuses on associating previously identified sleep genetic loci with macro- and micro- sleep architecture measurements in deep-phenotyped samples to understand the neurophysiological background and dissect the genetic subtypes underlying a sleep trait. We also perform polygenic risk score, genetic correlation, and Mendelian randomization analyses to explore the shared genetic underpinnings and causal connections with cardiometabolic and neuropsychiatric outcomes.
Sleep is a critical regulator of the body’s internal state, including epigenetics, gene expression, and metabolite profile. Conversely, the internal state of the body can also influence sleep. To uncover the intricate relationships between sleep and the body’s internal state at the molecular level, we perform multi-omics (transcriptomics, methylomics, metabolomics, and proteinomics) association analyses using data collected from the NHLBI TOPMed Program. Our current research focuses on developing novel pathway-based approaches to understand tissue-specific transcriptional regulation effects using transcriptomics data. We have recently identified a potential causal association between up-regulated heme biosynthesis pathway transcripts with increased sleep apnea severity. In addition, we are investigating the bi-directional link between metabolites and sleep outcomes, utilizing both targeted and untargeted platforms that measure over 1000 metabolites.
Technological advances of wearable devices have resulted in a significant boost of objective data collection on sleep and physical activity (PA) for multiple days. However, accelerometry data collection and processing are frequently done using different devices and protocols, regarding voltage signal modalities, quality controls, and scoring algorithms, etc. To address this issue, we lead the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium Accelerometry working group and collaborate with world-leading experts in sleep and PA. Our aim to develop standard data harmonization pipelines, identify human sleep-movement clusters, and investigate the role of accelerometry derived sleep, circadian, and activity metrics in the development of age-related common diseases.
Collaborating with the CHARGE Gene-Life Interaction working group, we lead the largest cross population meta-analysis of gene-sleep interaction analyses on multiple cardiometabolic traits (including blood pressure, blood lipids, obesity, and glycemic traits). These analyses identified multiple novel loci for cardiometabolic traits and novel genetic pathways modified by long and short sleep duration.
We investigate bi-directional associations between sleep and diet quality using longitudinal cohort and Mendelian randomization study designs to identify potential causal effects. Small clinical trials conducted in healthy individuals over short time periods have identified detrimental effects of a poor night’s sleep on next-day diet quality. Other experimental studies in similar settings suggest some foods may be sleep promoting. Our research adds to this evidence by establishing these effects in large population-based cohort studies with data on sleep and diet prospectively collected over longer time periods. We also investigate the interactive effects between sleep and diet patterns on cardiovascular diseases. Our results will help identify higher risk subgroups, elucidate mechanisms that contribute to population health inequities, and improve the effectiveness of chronic disease prevention strategies.