Anxiety and other mood disorders, such as major depressive disorder (MDD) and seasonal affective disorder (SAD), affect nearly one-fifth of the global population and disproportionately affect young adults. Individuals affected by mood disorders are frequently plagued by sleep and circadian problems, and recent genetic studies provide ample support for the association of circadian and sleep syndromes with depression and anxiety. Mathematical modeling has been crucial in understanding some of the essential features of the mammalian circadian clock and is now a vital tool for dissecting how circadian genes regulate the molecular mechanisms that influence mood. Here, we model the effect of five clock gene polymorphisms, previously linked to mood disorders, on circadian gene expression and, ultimately, on the period length and amplitude of the clock, two parameters that dictate the phase, or alignment, of the clock relative to the environment. We then test whether these gene variants are associated with circadian phenotypes (Horne-Ostberg Morningness-Eveningness scores) and well-established measures of depression (Beck Depression Inventory) and anxiety (State-Trait Anxiety Inventory) in a population of undergraduates ( n = 546). In this population, we find significant allelic and/or genotypic associations between CRY2 and two PER3 variants and diurnal preference. The PER3 length polymorphism (rs57875989) was significantly associated with depression in this sample, and individuals homozygous for the PER3 single nucleotide polymorphism (SNP) (rs228697) reported significantly higher anxiety. Our simple model satisfies available experimental knockdown conditions as well as existing data on clock polymorphisms associated with mood. In addition, our model enables us to predict circadian phenotypes (e.g., altered period length, amplitude) associated with mood disorders in order to identify critical effects of clock gene mutations on CRY/BMAL binding and to predict that the intronic SNPs studied represent gain-of-function mutations, causing increased transcription rate. Given the user-friendly structure of our model, we anticipate that it will be useful for further study of the relationships among clock polymorphisms, circadian misalignment, and mood disorders.