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. 2023 Dec 15;12(12):1532.
doi: 10.3390/biology12121532.

Risk for Seasonal Affective Disorder (SAD) Linked to Circadian Clock Gene Variants

Thanh Dang  1 , William A Russel  2 , Tazmilur Saad  1   3 , Luvna Dhawka  4 , Ahmet Ay  2   3 , Krista K Ingram  2
Affiliations

Risk for Seasonal Affective Disorder (SAD) Linked to Circadian Clock Gene Variants

Thanh Dang et al. Biology (Basel). .

Abstract

Molecular pathways affecting mood are associated with circadian clock gene variants and are influenced, in part, by the circadian clock, but the molecular mechanisms underlying this link are poorly understood. We use machine learning and statistical analyses to determine the circadian gene variants and clinical features most highly associated with symptoms of seasonality and seasonal affective disorder (SAD) in a deeply phenotyped population sample. We report sex-specific clock gene effects on seasonality and SAD symptoms; genotypic combinations of CLOCK3111/ZBTB20 and PER2/PER3B were significant genetic risk factors for males, and CRY2/PER3C and CRY2/PER3-VNTR were significant risk factors for females. Anxiety, eveningness, and increasing age were significant clinical risk factors for seasonality and SAD for females. Protective factors for SAD symptoms (in females only) included single gene variants: CRY1-GG and PER3-VNTR-4,5. Clock gene effects were partially or fully mediated by diurnal preference or chronotype, suggesting multiple indirect effects of clock genes on seasonality symptoms. Interestingly, protective effects of CRY1-GG, PER3-VNTR-4,5, and ZBTB20 genotypes on seasonality and depression were not mediated by chronotype, suggesting some clock variants have direct effects on depressive symptoms related to SAD. Our results support previous links between CRY2, PER2, and ZBTB20 genes and identify novel links for CLOCK and PER3 with symptoms of seasonality and SAD. Our findings reinforce the sex-specific nature of circadian clock influences on seasonality and SAD and underscore the multiple pathways by which clock variants affect downstream mood pathways via direct and indirect mechanisms.

Keywords: SAD; chronobiology; circadian clock; depression; machine learning; molecular clockwork; mood disorders; seasonal affective disorder; seasonality.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Classifiers exhibit greater accuracy increase from baseline when stratified by sex. (a) Analyses on the overall dataset yielded up to 6% greater accuracy than the baseline accuracy (56.5%) using the support vector machine (SVM) classifier and the joint mutual information (JMI) feature selection. (b) Prediction of male seasonality yielded up to 10% greater accuracy than the baseline accuracy (58%) using the RF classifier and correlation-based feature selection. (c) Prediction of female seasonality yielded up to 7% greater accuracy than the baseline accuracy (62.2%) using the LR and SVM classifiers and mRMR feature selection.
Figure 2
Figure 2
Two-way ANOVA and Tukey post hoc analysis reveal that the PER2-GG/PER3B-AG combination is predictive of seasonality in males. Average SPAQ scores were higher in males with the PER2-GG/PER3B-AG genotypic combination than in males with other genotypic combinations (Genotype: F1,217 = 4.312, p = 0.039; Gender: F1,217 = 0.005, p = 0.946; Genotype × Gender: F1,217 = 1.650, p = 0.200). Error bars denote SE. A, B denote significant differences for Tukey post hoc analyses.
Figure 3
Figure 3
Two-way ANOVA and Tukey post hoc analysis reveal risk and protective combinations for seasonality in females. (a) Significantly higher average SPAQ scores were identified in females with the CRY2-AA/PER3C-TG combination relative to other genotypic combinations (Genotype: F1,217 = 4.084, p = 0.044; Gender: F1,217 = 5.496, p = 0.020; Genotype × Gender: F1,217 = 0.449, p = 0.504). (b) Average SPAQ scores were lower for males and females with the PER2-AA/CRY1-GG combination (Genotype: F1,217 = 7.001, p = 0.009; Gender: F1,217 = 3.735, p = 0.055; Genotype × Gender: F1,217 = 0.192, p = 0.662). (c) Average SPAQ scores were lower for females with the PER2-AA/PER3C-TT combination than for females with other combinations (Genotype: F1,217 = 3.420; p = 0.066, Gender: F1,217 = 0.182; p = 0.670, Genotype × Gender: F1,217 = 2.257, p = 0.134). (d) Average SPAQ scores were lower for males and females with VNTR-5,5/CRY1-GG combination (Genotype: F1,217 = 3.525, p = 0.062; Gender: F1,217 = 4.417, p = 0.037; Genotype x Gender: F1,217 = 0.755, p = 0.396) (N = 221). A, B denote significant differences for Tukey post hoc analyses.
Figure 3
Figure 3
Two-way ANOVA and Tukey post hoc analysis reveal risk and protective combinations for seasonality in females. (a) Significantly higher average SPAQ scores were identified in females with the CRY2-AA/PER3C-TG combination relative to other genotypic combinations (Genotype: F1,217 = 4.084, p = 0.044; Gender: F1,217 = 5.496, p = 0.020; Genotype × Gender: F1,217 = 0.449, p = 0.504). (b) Average SPAQ scores were lower for males and females with the PER2-AA/CRY1-GG combination (Genotype: F1,217 = 7.001, p = 0.009; Gender: F1,217 = 3.735, p = 0.055; Genotype × Gender: F1,217 = 0.192, p = 0.662). (c) Average SPAQ scores were lower for females with the PER2-AA/PER3C-TT combination than for females with other combinations (Genotype: F1,217 = 3.420; p = 0.066, Gender: F1,217 = 0.182; p = 0.670, Genotype × Gender: F1,217 = 2.257, p = 0.134). (d) Average SPAQ scores were lower for males and females with VNTR-5,5/CRY1-GG combination (Genotype: F1,217 = 3.525, p = 0.062; Gender: F1,217 = 4.417, p = 0.037; Genotype x Gender: F1,217 = 0.755, p = 0.396) (N = 221). A, B denote significant differences for Tukey post hoc analyses.
Figure 4
Figure 4
Association rule networks for male and female seasonality. (a) The most frequently occurring SNP variants for males included VNTR-4,5, PER3C-TG, PER2-GG, and CRY1-CC, while age and being lower-middle class (LMC) appeared as important clinical factors. (b) The most frequently occurring SNP variants for females were VNTR-4,5, PER3C-TG, PER2-GG, and CRY1-CC, while age, being upper-middle class (UMC), and eveningness commonly appeared as important clinical factors.
Figure 5
Figure 5
Venn diagram summarizing significant genotypic and clinical features for seasonality in males and females resulting from multivariate linear regression, univariate logistic regression, multivariate logistic regression, and association rule learning network results. Venn diagram comparisons were constructed from male and female seasonality analysis, risk factors were denoted by red text, and protective factors were denoted by silver text. (a) Genotypic factor comparison reveals shared and exclusive risk factors between males and females. (b) Clinical factor comparison reveals shared and exclusive risk factors between males and females.
Figure 6
Figure 6
Venn diagram summarizing significant genotypic features for female seasonality and female SAD resulting from multivariate linear regression, univariate logistic regression, multivariate logistic regression, and association rule learning network results. Venn diagram comparisons were constructed from our female seasonality analysis and our SAD regression results, risk factors were denoted by red text, and protective factors were denoted by silver text. (a) Genotypic factor comparison reveals risk factors exclusive to seasonality. (b) Clinical factor comparison reveals shared and exclusive risk factors between seasonality and SAD in females.
Figure 7
Figure 7
ARACNE networks for seasonality in male and female population subsections. ARACNE networks were constructed for seasonality in (a) males and (b) females using MEQ as a potential mediator variable.
Figure 8
Figure 8
ARACNE networks for depression in (a) male and (b) female population subsections. ARACNE networks were constructed for depression in males and females using MEQ as a potential mediator variable.
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