Abstract 24-hour biological rhythms are essential to maintain physiological homeostasis. Disruption of these rhythms increases the risks of multiple diseases. Biological rhythms are known to have a genetic basis formed by core clock genes, but how individual genetic variation shapes the oscillating transcriptome and contributes to human chronophysiology and disease risk is largely unknown. Here, we mapped interactions between temporal gene expression and genotype to identify quantitative trait loci (QTLs) contributing to rhythmic gene expression. These newly identified QTLs were termed as rhythmic QTLs (rhyQTLs), which determine previously unappreciated rhythmic genes in human subpopulations with specific genotypes. Functionally, rhyQTLs and their associated rhythmic genes contribute extensively to essential chronophysiological processes, including bile acid and lipid metabolism. The identification of rhyQTLs sheds light on the genetic mechanisms of gene rhythmicity, offers mechanistic insights into variations in human disease risk, and enables precision chronotherapeutic approaches for patients. Subject terms: Metabolic diseases, Quantitative trait, Genome-wide association studies __________________________________________________________________ Circadian rhythms influence key physiological functions. Here, the authors defined rhythmic quantitative trait loci that reveal novel genotype-specific rhythmic genes, explaining individual variations in rhythmic gene expression and disease risk. Introduction Biological rhythms refer to recurring physiological processes with a periodicity of approximately 24 h. These rhythms allow mammals to anticipate daily environmental changes, including light-dark cycles, and are essential to maintain physiological homeostasis^[31]1–[32]3. The disruption of biological rhythms is increasingly recognized as a risk factor for multiple diseases, including type 2 diabetes, cardiovascular disease, digestive disease, and cancer^[33]4–[34]6. It is generally accepted that variations in the 24 hrhythms regarding gene expression and physiological processes exist in different individuals^[35]7,[36]8. Nevertheless, the major questions remain regarding whether and how these variations in biological rhythms contribute to disease risk, as well as what underlying mechanisms are responsible for these variations. Moreover, our current understanding of mechanisms regulating rhythmic gene expression highlights the regulatory roles of transcription factors (TFs) on cis-regulatory elements (CREs), including core clock components such as BMAL1 and REV-ERBs, as well as noncanonical clock TFs^[37]9–[38]11. Genetic or environmental perturbation of these regulatory TFs in animal models can cause or exacerbate multiple diseases^[39]12,[40]13. Variants in putative CREs have been linked to variations in human gene regulation, sleep disorders, individual chronotypes, and other complex traits and diseases^[41]14–[42]16. Although these variants in CREs may affect core clock gene expression, the specific TFs targeting these CREs and their mechanisms of action remain poorly defined. Moreover, the relationships between genetic variation and human biological rhythms in specific tissues are largely unexplored, as are the mechanisms linking these variations to complex traits and diseases in humans. Since direct genetic manipulation is impractical in humans, we studied natural genetic variation in the Genotype Tissue Expression (GTEx) Project to identify associations with perturbations in gene rhythmicity, which refers to the periodic fluctuation of gene expression with a 24-hour cycle, and to further determine the relationship between gene rhythmicity and human phenotype in various tissues. Results We first established a median of 39 million genetic variant-gene pairs across 45 tissues from 838 individuals using GTEx data (Supplementary Data [43]1)^[44]17 and then assessed the association between genetic variants and rhythmic expression of their nearby genes (Fig. [45]1a). Using harmonic regression to evaluate the gene rhythmicity in a subpopulation with specific genotype for each genetic variant-gene pair, we identified a median of 3200 genes across 45 tissues that are rhythmically expressed in at least one genotype subpopulation. A median of 2044, accounting for 63.8% of total rhythmic genes across tissues, exhibits differential rhythmic expression among genotypes (Fig. [46]1a and Supplementary Fig. [47]1a–[48]c). For example, the expression of allograft inflammatory factor 1 (AIF1) in heart tissue, which correlates with the development of cardiac allograft^[49]18,[50]19, is only rhythmic in the subpopulation with GG genotype at single nucleotide polymorphism (SNP) rs7740525 (Fig. [51]1b). Note that the overall expression levels of AIF1 have no difference across these three genotypes. This result indicates the relationship between SNP rs7740525 and AIF1 rhythmicity cannot be explained by expression quantitative trait loci (eQTLs), which are associated with variations in gene expression abundance across different genotypes. Here, we refer to the genetic loci that are associated with variations in 24-hour rhythmic gene expression as rhythmic quantitative trait loci (rhyQTLs) and the genes associated with at least one rhyQTL as rhyGenes. In line with previous studies, we identified rhyQTLs that are associated with chronotype^[52]15,[53]20 (Supplementary Fig. [54]2a). For example, rs10788872 and rs2055975 are linked to individuals who prefer morning activities, such as going to bed and waking earlier^[55]15. These SNPs are associated with the rhythmic expression of the Circadian Associated Repressor of Transcription (CIART) in the brain tissues (Supplementary Fig. [56]2b, c), which regulate circadian rhythms by modulating the activity of key circadian clock components^[57]20. This association suggests that changes in the rhythmic expression of CIART may reflect the genetic basis of chronotype preferences. We also detected rhyQTLs