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Depression and bipolar disorder subtypes differ in their genetic correlations with biological rhythms

Major Depression and Bipolar Disorder Type I (BIP-I) and Type II (BIP-II), are characterized by depressed, manic, and hypomanic episodes in which specific changes of physical activity, circadian rhythm, and sleep are observed. It is known that genetic factors contribute to variation in mood disorders and biological rhythms, but unclear to what extent there is an overlap between their underlying genetics. In the present study, data from genome-wide association studies were used to examine the genetic relationship between mood disorders and biological rhythms. We tested the genetic correlation of depression, BIP-I, and BIP-II with physical activity (overall physical activity, moderate activity, sedentary behaviour), circadian rhythm (relative amplitude), and sleep features (sleep duration, daytime sleepiness). Genetic correlations of depression, BIP-I, and BIP-II with biological rhythms were compared to discover commonalities and differences. A gene-based analysis tested for associations of single genes and common circadian genes with mood disorders. Depression was negatively correlated with overall physical activity and positively with sedentary behaviour, while BIP-I showed associations in the opposite direction. Depression and BIP-II had negative correlations with relative amplitude. All mood disorders were positively correlated with daytime sleepiness. Overall, we observed both genetic commonalities and differences across mood disorders in their relationships with biological rhythms: depression and BIP-I differed the most, while BIP-II was in an intermediate position. Gene-based analysis suggested potential targets for further investigation. The present results suggest shared genetic underpinnings for the clinically observed associations between mood disorders and biological rhythms. Research considering possible joint mechanisms may offer avenues for improving disease detection and treatment.


Materials and methods
GWAS samples. Table 1 gives an overview of the GWAS summary statistics and cohorts used in this analysis. Summary statistics comprise aggregate p-values and association data for every single nucleotide polymorphism (SNP) analysed in a GWAS 34 .
For depression, the latest publicly available summary statistics were used as described in 37 , the meta-analysis comprised 33 cohorts (excluding 23andme cohort), resulting in summary statistics of 170,756 depression cases and 329,443 controls. Summary statistics for BIP-I and BIP-II were derived from the latest Psychiatric Genomics Consortium GWAS on BIP consisting of 57 cohorts with a total number of 41,917 BIP cases and 371,549 controls, including 25,060 BIP-I, 6,781 BIP-II cases 38 .
Biological rhythms summary data were obtained from studies carried out in the UK Biobank (UKB) cohort. The UKB is a national cohort consisting of over 500,000 participants (40-69 years old) with genetic and deep phenotype data 41 . Biological rhythms were assessed in a subset of the UKB cohort including 103,720 individuals who wore accelerometers for 7 days. Based on this accelerometer data the UKB accelerometer expert working group extracted or classified several physical activity parameters: overall physical activity (measuring the mean activity over the whole assessment), moderate activity (activities requiring higher levels of energy such as exercising), sedentary behaviour (activities requiring low energy consumption like sitting or lying), relative amplitude (for circadian rhythm), and sleep duration (the time spent in behaviour classified as sleep) 11 . Daytime sleepiness was assessed subjectively in the whole UKB sample with a self-reported question, asking participants if they were Scientific Reports | (2022) 12:15740 | https://doi.org/10.1038/s41598-022-19720-5 www.nature.com/scientificreports/ likely to doze off or fall asleep during the day unintentionally (rating on a 4-point scale) 35 . The current analysis used summary data from GWASs based on these biological rhythm parameters were made available by the UKB. All original studies and used datasets are included in the Data Availability section, where detailed information on cohorts and datasets can be found. Genetic correlation analysis. Genetic correlations of depression, BIP-I, and BIP-II, with overall physical activity, moderate activity, sedentary behaviour, sleep duration, relative amplitude, and daytime sleepiness were calculated using the bivariate genetic correlation method in Linkage Disequilibrium Score Regression (LDSC) software (https:// github. com/ bulik/ ldsc). LDSC is a regression-based analysis tool that can estimate the genetic correlation between two traits using GWAS summary statistics 42 . We used the Linkage Disequilibrium (LD) Scores of the 1,000 Genomes Project for use with European samples. Default settings were applied for the filtering process: information metric (INFO score) > 0.9, indicating high quality of imputation, Minor Allele Frequency (MAF) > 0.01, and removing out of bounds summary statistic p-values. Indels, ambiguous, duplicated SNPs, and SNPs having no match in the LD Score reference panel were removed. SNP heritability analyses using LDSC revealed a Z-score of > 4 for all used GWAS summary statistics (see Tables S1.1-S1.3) indicating good interpretability and power for genetic correlation analyses, as recommended by the authors of LDSC 43 . For each mood disorder (depression, BIP-I, and BIP-II) genetic correlations with the six biological rhythm traits were estimated for which Bonferroni correction (p < 0.05/6 = 0.0083) was applied.

Analysis of differences in correlations.
To compare the above calculated correlations between depression, BIP-I, and BIP-II, we employed the block jackknife extension of LDSC 42 , as previously described in 44 . A z-test was conducted using the LDSC extension computing a z-value (z), standard error (SE), and p-value for each comparison. The following comparisons were analysed: depression vs. BIP-I, depression vs. BIP-II, and BIP-I versus BIP-II in their correlations with biological rhythm traits. For each comparison, six tests were conducted and Bonferroni correction (p < 0.05/6 = 0.0083) for the number of biological rhythm traits was applied.
Gene association analysis and targeted examination of circadian genes. A genome-wide genebased analysis for mood disorders and biological rhythms was conducted using the Multi-marker Analysis of GenoMic Annotation (MAGMA) 45 . MAGMA is a computational tool that allows gene-based and geneset analysis of GWAS data and is widely used to detect genes and pathways associated with phenotypes and diseases. The gene-based analysis is based on a multiple regression model, using data of all single variants annotated to a gene, deriving one p-value per gene to determine if the genetic variation in this gene is associated with the phenotype (for further details see 45 ).
Default settings were used. Lists of genes were reduced to the number of genes available in all mood disorders and biological rhythms summary statistics (n = 17,861 genes). Bonferroni correction was applied for all tested genes (p < 0.05/17,861 = 2.80 × 10 -6 ) to determine genes significantly associated with mood disorders and biological rhythms.
The overlap of genes associated with each of the mood disorders and biological rhythms was then examined based on filtered gene sets with a suggestive threshold of p < 1 × 10 -5 .

Results
Genetic correlations of depression, BIP-I, and BIP-II with biological rhythm traits and the differences between the mood disorders in their correlations are presented in Fig. 1. Bonferroni corrected p-values are indicated in Genetic correlations of depression with biological rhythms. Significant negative correlations of depression with overall physical activity (r g = − 0.11, p = 0.0001), moderate activity (r g = − 0.09, p = 0.04) and with relative amplitude (r g = − 0.30, p = 4.40 × 10 -12 ) were found. A significant positive correlation of depression with www.nature.com/scientificreports/ daytime sleepiness (r g = 0.17, p = 3.17 × 10 -11 ) was observed. Depression was not significantly correlated with sedentary behaviour (r g = 0.03, p = 0.40), and sleep duration (r g = 0.05, p = 0.11) (see Table S1.1).
BIP-I and BIP-II differed significantly in their correlation with relative amplitude (z = 2.17, p = 0.03) and daytime sleepiness (z = − 2.31, p = 0.02). Comparing correlations of BIP-I and BIP-II with respect to overall physical activity, moderate activity, sedentary behaviour, sleep duration yielded no significant differences (see Table S2.3).

Discussion
In the present study, we used data from independent GWASs of mood disorders and biological rhythms and demonstrate that they have common genetic roots. Our results show that depression, BIP-I, BIP-II have commonalities but also distinct differences in their genetic correlations with biological rhythms. Notably, our study is the first to differentiate between the BIP subtypes BIP-I and BIP-II, revealing specific patterns of the two subtypes with biological rhythms. Our gene-level analysis shows that depression and BIP-I share genes with biological rhythms and that both mood disorders are associated with known circadian genes. Epidemiological and clinical studies have repeatedly reported an inverse relation between higher levels of activity and depression 46,47 . Our findings now show that this inverse relationship has -at least partially-a common genetic basis. Our finding of a negative genetic correlation between increased physical activity (moderate activity) and depression is in line with the results from a recent twin study showing a genetic relationship between decreased depression and increased activity 26 , and also confirms an earlier study using GWAS data which reported negative genetic correlations of MD with overall physical activity as well as walking 36 .
BIP-I and BIP-II were positively genetically correlated with moderate activity and negatively with sedentary behaviour, implying that both BIP-I and BIP-II share genetic properties with increased physical activity. Our findings agree with previous findings showing a positive genetic correlation of BIP with moderate activity and walking 36 based on an earlier BIP GWAS 48 . Placing the present findings in context, it should be noted that earlier works are based on BIP GWAS aggregating all subtypes in wherein the large majority of the patients were diagnosed with BIP-I 48 ; the present analysis uses a larger dataset and separately examines BIP-II. Actigraphy studies suggest positive associations between physical activity and BIP during manic and hypomanic phases 9,10 ; our results indicate that those relationships are partially due to shared genetic factors and not solely a result of the mood state in which they are observed.
Interestingly, our investigation of relative amplitude found negative genetic correlations with both depression and BIP-II, but not with BIP-I, which is a reflection of findings at the phenotypic level [13][14][15][16] . We note that the strongest and most statistically significant correlation in the present analysis was between depression and relative www.nature.com/scientificreports/ amplitude, suggesting this relationship as a target for further investigation and characterization. Physiological and molecular studies suggest that disruption of the molecular clock is strongly involved in mood symptoms observed in MD and BIP 30,49,50 ; the present findings appear to suggest a connection on a wider genomic level. One study recently took such an approach using genome-wide data to investigate these relationships with polygenic risk scores (PRS) 40 . A PRS is a single score capturing the sum of an individual's risk alleles weighted by GWAS summary statistics to quantify the genetic predisposition of an individual for a certain phenotype 34 . This study found that participants with an increased PRS for a disrupted circadian rhythm showed in turn an increased PRS for MD 40 . It should be noted that the measure relative amplitude, on which the summary statistics were based, is a composite index representing the difference between most and least active hours of the day and does not fully describe rest-activity cycles. More comprehensive measures will be needed to better investigate the links between circadian rhythmicity and mood disorders. Symptoms such as insufficient sleep duration and frequent awakenings are often reported in mood disorders 51 . Genetic studies report a high genetic overlap of sleep-related phenotypes (e.g., insomnia, chronotype, sleep duration) with MD and BIP 52,53 . An earlier GWAS using self-report measures of sleep duration reported a significant genetic correlation with BIP, but not with depressive symptoms 54 ; in the present study, significant genetic correlations of objectively measured sleep duration were not observed with MD or either BIP subtypes. This may be due to the difference in assessment methodology but may also be clarified when larger samples of objectively measured sleep duration become available. Here, we observed that depression, BIP-I, and BIP-II are significantly positively correlated with daytime sleepiness. With respect to MD, this confirms findings of an earlier study that found a genetic correlation between daytime sleepiness and depressive symptoms 35 . The same study examined BIP as well in a smaller sample but did not look at the subtypes separately 35 ; the increased sample size and power in the present study enabled detection of significant correlations of daytime sleepiness in BIP-I and BIP-II, which is a novel finding.
When we compared the genetic correlations between mood disorders, the most prominent differences were found between depression and BIP-I, which showed genetic correlations with opposite directions for all objectively assessed traits besides relative amplitude, although in the same direction, correlation strength differed significantly. These differences may be causally linked to the clinical symptoms observed during the depressive episodes in MD (generally low activity) and during manic episodes (high activity) in BIP-I. BIP-II showed an intermediate correlational pattern, with the only significant differences in relative amplitude and daytime sleepiness being with BIP-I and a significant difference in moderate activity to depression. Notably, depression and BIP-II showed a closer resemblance in sleep duration, relative amplitude, and daytime sleepiness than BIP-I and BIP-II, which suggests a stronger link between genetics of depressed features with these phenotypes. At the same time, BIP-I and BIP-II are more similar to each other when compared to depression with respect to increased activity phenotypes. It is of interest that the strongest similarities between the mood disorders were observed in daytime sleepiness, suggesting that it shares common genetic etiology with all mood disorder types. In summary, the discovered similarities and differences appear to be clues to delineating these mood disorders with respect to each other.
Analysis of overlapping genes suggests potential pleiotropic targets for the investigation of mood disorders and biological rhythms. Depression and sedentary behaviour showed an overlap in MEF2C, a protein-coding gene involved in processes such as cell differentiation and neurogenesis; studies implicate MEF2C in neuropsychiatric disorders (e.g., schizophrenia and autism) 55 and it is also involved in muscle activity and exercise 56 . CCDC36, the gene overlapping between depression and relative amplitude, is important for meiosis and other cellular processes; it has been reported to contribute to both MD and attention deficit and hyperactivity disorder 57 . The overlapping gene between depression and daytime sleepiness was ERBB4, part of the Tyr protein kinase family, was found to be associated with schizophrenia endophenotypes 58 . Also, a study investigating sleep regulation via the EGFR signalling pathway found genetic variants in ERBB4 to be associated with excessive daytime sleepiness 59 . BIP-I and sleep duration were both associated with MSRA, which is engaged in protein repair processes; MSRA was associated with nine psychiatric disorders in a study investigating pleiotropic effects 60 ; it has also been linked to neuroticism 61 and sleep behaviour 62 . CADM2, which was overlapping in BIP-I and daytime sleepiness, belongs to the immunoglobulin superfamily and has been implicated in various genetic studies investigating psychological and physical traits, such as obesity 63 , cognitive function 64 , and physical activity 65 .
Findings from the targeted examination of clock genes revealed several significant associations with depression (NR1D1 and PER1) and BIP-I (ARNTL, CRY2, RORB) underlining the close relationship with mood disorders. NR1D1 regulates the expression of core clock genes and is involved in metabolic and immunological processes; it has been reported to be associated with both depression 66 and bipolar disorder 67 . PER1 is involved in the transcription and translation of core clock components and is involved in a wide range of physiological functions (e.g.metabolism, sleep, and the endocrine and immune systems); changes in PER1 expression have been found in patients with depression compared to healthy controls 67 . ARNTL acts as an activator of transcriptional processes in the circadian clock machinery and is known as a metabolic modulator 68 ; it has been implicated in affective disorders 67 . CRY2, a repressor of transcription in the circadian clock machinery, has been shown to be related to depression 69 , and bipolar disorder 70 . The nuclear receptor RORB has been shown to be associated with bipolar disorder 71 . Even though circadian genes did not reach genome-wide significance, they support findings from previous studies indicating a close genetic relationship between mood disorders and circadian rhythms on a genome-wide gene level.
The present study had certain limitations. We used the largest currently available cohorts for the respective phenotypes; however, sample sizes and statistical power of the GWASs differed. In particular, BIP-II and relative www.nature.com/scientificreports/ amplitude samples were smaller than for the other GWASs. All GWASs included exceed the SNP heritability Z-score threshold of 4 which is recommended for genetic correlation analyses (see Tables S1.1-S1.3) and we could expect more significant findings with larger samples. Another limitation that bears mentioning is that it cannot be excluded that some of the individuals in the biological rhythms dataset were suffering from a mood disorder (lifetime or acute) 72 . However, in the light of the relatively low lifetime frequency of BIP and the decreased likelihood that individuals with MD or BIP have participated in the accelerometer recording during an acute mood episode, it is unlikely that the genetic correlation between biological rhythms and mood disorders could result from the biorhythms observed in these individuals at the time of assessment. These results show that clinically observed relationships of mood disorders and biological rhythms have a common genetic basis, and indicate that alterations in biological rhythms observed in mood disorder patients are linked to the genetic vulnerability for the specific disorder, and not only to the current mood episode. Biological rhythms should be given more attention in the study of mood disorders and require greater consideration with respect to assessment and treatment. Links between mood disorders and genetic components underlying other physiological traits also under circadian control (e.g., hormonal secretion, body temperature) should be investigated to enhance our understanding of this complex relationship. As sample sizes of GWASs increase, overlapping genes and pathways can be identified with more certainty, enabling investigation of the causality of the observed genetic relationship between mood disorders and biological rhythms. To further extend these findings, future studies in the field could investigate this relationship also on the individual level, assessing mood disorders and biological rhythm phenotypes with more refined approaches such as ambulatory assessment, while at the same time incorporating genetic data. Furthermore, incorporating epigenetic and gene expression assessments into research designs will allow exploration of underlying mechanisms, in particular those that change over time. Implementation of these multimodal assessments will not only give deeper insight into shared mechanisms but also shed light on how they interact with the environment. If shared genetic factors jointly affect biological rhythms and liability to mood disorders, further investigation may allow targeting of these factors in treatment, providing potential avenues of improvement for therapeutic approaches.

Code availability
Code used for this analysis can be found here: https:// github. com/ bulik/ ldsc/ wiki/ Herit abili ty-and-Genet ic-Corre lation (retrieved February 4th, 2022) and https:// ctg. cncr. nl/ softw are/ magma (retrieved July 16th, 2022) or can be made available by the corresponding author based on reasonable request. www.nature.com/scientificreports/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/.