Search PubMedSearch

PubMed · 41325697

Genome-wide gene-sleep interaction study identifies novel lipid loci in 732,564 participants.

Raymond Noordam·Wenyi Wang·Pavithra Nagarajan·Heming Wang·Michael R Brown·Amy R Bentley·Qin Hui·Aldi T Kraja·John L Morrison·Jeffrey R O'Connel·Songmi Lee·Karen Schwander·Traci M Bartz·Lisa de Las Fuentes·Mary F Feitosa·Xiuqing Guo·Xu Hanfei·Sarah E Harris·Zhijie Huang·Mart Kals·Christophe Lefevre·Massimo Mangino·Yuri Milaneschi·Peter J van der Most·Natasha L Pacheco·Nicholette D Palmer·Varun Rao·Rainer Rauramaa·Quan Sun·Yasuharu Tabara·Dina Vojinovic·Yujie Wang·Stefan Weiss·Qian Yang·Wei Zhao·Wanying Zhu·Md Abu Yusuf Ansari·Hugues Aschard·Pramod Anugu·Themistocles L Assimes·John Attia·Laura D Baker·Christie Ballantyne·Lydia Bazzano·Eric Boerwinkle·Brain Cade·Hung-Hsin Chen·Wei Chen·Yii-Der Ida Chen·Zekai Chen·Kelly Cho·Ileana De Anda-Duran·Latchezar Dimitrov·Anh Do·Todd Edwards·Tariq Faquih·Aroon Hingorani·Susan P Fisher-Hoch·J Michael Gaziano·Sina A Gharib·Ayush Giri·Mohsen Ghanbari·Hans Jörgen Grabe·Mariaelisa Graff·C Charles Gu·Jiang He·Sami Heikkinen·James Hixson·Yuk-Lam Ho·Michelle M Hood·Serena C Houghton·Carrie A Karvonen-Gutierrez·Takahisa Kawaguchi·Tuomas O Kilpeläinen·Pirjo Komulainen·Henry J Lin·Gregorio V Linchangco·Annemarie I Luik·Jintao Ma·James B Meigs·Joseph B McCormick·Cristina Menni·Ilja M Nolte·Jill M Norris·Lauren E Petty·Hannah G Polikowsky·Laura M Raffield·Stephen S Rich·Renata L Riha·Thomas C Russ·Edward A Ruiz-Narvaez·Colleen M Sitlani·Jennifer A Smith·Harold Snieder·Tamar Sofer·Botong Shen·Jingxian Tang·Kent D Taylor·Maris Teder-Laving·Rima Triatin

Abstract

BACKGROUND AND AIMS: Deviations from the population mean in sleep duration have been associated with increased risk for developing dyslipidemia and atherosclerotic cardiovascular disease, but the mechanism of effect is poorly characterized. We performed large-scale genome-wide gene-sleep interaction analyses of lipid levels to identify genetic variants underpinning the biomolecular pathways of sleep-associated lipid disturbances and to suggest possible druggable targets. METHODS: We collected data from 55 cohorts with a combined sample size of 732,564 participants (87&#xa0;% European ancestry) with data on lipid traits (high-density lipoprotein [HDL-c] and low-density lipoprotein [LDL-c] cholesterol and triglycerides [TG]). Short (STST) and long (LTST) total sleep time were defined by the extreme 20&#xa0;% of the age- and sex-standardized values within each cohort. Based on cohort-level summary statistics data, we performed meta-analyses for one-degree of freedom tests of interaction and two-degree of freedom joint tests of the SNP-main and -interaction effect on lipid levels. RESULTS: The one-degree of freedom variant-sleep interaction test identified 10 novel loci (Pint<5.0e-9), and we additionally identify 7 loci within the two-degree of freedom analyses (Pjoint<5.0e-9 in combination with Pint<6.6e-6). Multiple loci, including those mapped to APSH (target for aspartic and succinic acid) and SLC8A1 showed biological plausibility and druggability potential based on literature. CONCLUSIONS: Collectively, the 17 (9 with short and 8 with long sleep) loci provided evidence into the biomolecular mechanisms underlying sleep-associated lipid changes, including potential involvement of the vitamin D receptor pathway. Collectively, these findings may contribute developing novel interventions for treating dyslipidemia in people with sleep disturbances.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Raymond Noordam, Wenyi Wang, Pavithra Nagarajan, Heming Wang, Michael R Brown, Amy R Bentley, Qin Hui, Aldi T Kraja, John L Morrison, Jeffrey R O'Connel, Songmi Lee, Karen Schwander, Traci M Bartz, Lisa de Las Fuentes, Mary F Feitosa, Xiuqing Guo, Xu Hanfei, Sarah E Harris, Zhijie Huang, Mart Kals, Christophe Lefevre, Massimo Mangino, Yuri Milaneschi, Peter J van der Most, Natasha L Pacheco, Nicholette D Palmer, Varun Rao, Rainer Rauramaa, Quan Sun, Yasuharu Tabara, Dina Vojinovic, Yujie Wang, Stefan Weiss, Qian Yang, Wei Zhao, Wanying Zhu, Md Abu Yusuf Ansari, Hugues Aschard, Pramod Anugu, Themistocles L Assimes, John Attia, Laura D Baker, Christie Ballantyne, Lydia Bazzano, Eric Boerwinkle, Brain Cade, Hung-Hsin Chen, Wei Chen, Yii-Der Ida Chen, Zekai Chen, Kelly Cho, Ileana De Anda-Duran, Latchezar Dimitrov, Anh Do, Todd Edwards, Tariq Faquih, Aroon Hingorani, Susan P Fisher-Hoch, J Michael Gaziano, Sina A Gharib, Ayush Giri, Mohsen Ghanbari, Hans J&#xf6;rgen Grabe, Mariaelisa Graff, C Charles Gu, Jiang He, Sami Heikkinen, James Hixson, Yuk-Lam Ho, Michelle M Hood, Serena C Houghton, Carrie A Karvonen-Gutierrez, Takahisa Kawaguchi, Tuomas O Kilpel&#xe4;inen, Pirjo Komulainen, Henry J Lin, Gregorio V Linchangco, Annemarie I Luik, Jintao Ma, James B Meigs, Joseph B McCormick, Cristina Menni, Ilja M Nolte, Jill M Norris, Lauren E Petty, Hannah G Polikowsky, Laura M Raffield, Stephen S Rich, Renata L Riha, Thomas C Russ, Edward A Ruiz-Narvaez, Colleen M Sitlani, Jennifer A Smith, Harold Snieder, Tamar Sofer, Botong Shen, Jingxian Tang, Kent D Taylor, Maris Teder-Laving, Rima Triatin. 2025-11-26. Genome-wide gene-sleep interaction study identifies novel lipid loci in 732,564 participants.. https://doi.org/10.1016/j.atherosclerosis.2025.120603

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans