Descriptions of GWAS study and cohort

We conducted a GWAS of self-reported morningness in the 23andMe participant cohort14, across a total of ∼8 million genotyped or imputed polymorphic sites. Morningness was defined by combining the highly concordant responses (Cohen’s Kappa=0.95, P<1.0 × 10−200) to two web based survey questions that ask if the individual is naturally a morning or night person (Supplementary Table 2). Among 135,447 who answered at least one survey, 75.5% were scored as morning or night persons. Individuals who provided neutral (n=32,842) or discordant responses (n=309) were removed (Supplementary Table 9). We did not find differences in age, gender or principal components (PCs; all P>0.01) when comparing individuals who provided discordant responses versus individuals who gave concordant responses (n=12,442). We included individuals of European ancestry who had consented for research, and related individuals were removed from analysis (Methods section). Morningness is significantly associated with gender (P=4.4 × 10−77), with a prevalence of 39.7% in males and 48.4% in females. Its prevalence increases with age (P<1.0 × 10−200): 24.2% of those under 30-years-old prefer mornings compared with 63.1% of those over 60. This age trend is consistent with previous reported observations15.

Table 1 (together with Supplementary Table 2) shows the marginal association between morningness and other sleep phenotypes, BMI and depression (defined in Supplementary Table 3). Morning persons are significantly less likely to have insomnia (12.9 versus 18.3%, odds ratio (OR)=0.66, P=2.4 × 10−74). They are also less likely to require >8 h of sleep per day (OR=0.67, P=1.1 × 10−72), to sleep soundly (OR=0.74, P=8.5 × 10−50), to sweat while sleeping (OR=0.8, P=1.0 × 10−23) and to sleep walk (OR=0.77, P=4.7 × 10−10). Morningness is also associated with lower prevalence of depression (OR=0.64, P=1.1 × 10−128, Supplementary Table 11). Morning persons are less prevalent in extreme BMI groups, namely the underweight (≤18.5) and the obese (≥30) group (Table 1, Supplementary Fig. 2). However, we found that after for adjusting for age and sex, the prevalence of morning persons decreases monotonically across increasing BMI categories (Supplementary Table 11).

Table 1 Demographic characteristics of the GWAS cohort. Full size table

We included age, sex and the first 5 PCs in a logistic regression model and computed likelihood ratio tests for association of each genotyped or imputed marker with morningness. Association test results were adjusted for a genomic inflation factor of 1.21 (Supplementary Data 1). For an equivalent study of 1,000 cases and 1,000 controls, the genomic inflation factor (known as λ 1,000 (ref. 16)) would be 1.005. The Manhattan plot (Fig. 1) shows 15 morningness-associated regions with genome-wide significance (P<5 × 10-8). Table 2 categorizes their index single nucleotide polymorphisms (SNPs) by nearby genes. We used Haploreg17, a web based computational tool to explore chromatin states, conservations and regulatory motif alterations using public databases, to understand the possible functional roles of these index SNPs (Supplementary Table 16 and Supplementary Data 2).

Figure 1: Manhattan plot of the GWAS of being a morning person. The grey line corresponds to P=5.0 × 10−8, and the results above this threshold are shown in red. Gene labels are annotated as the nearby genes to the significant SNPs. Full size image

Table 2 Index significant SNPs that are associated with being a morning person. Full size table

Genetic association analyses

Seven loci are near well-established circadian genes. rs12736689 (P=7.0 × 10−18) is in strong linkage disequilibrium (LD) (r2=0.89) with the nonsynonymous variant rs1144566 (H137R) of nearby gene RGS16 (Supplementary Fig. 3), a G protein signalling regulator that inactivates G protein alpha subunits. RGS16 knock-out mice were shown to have a longer circadian period18. rs9479402 (P=3.9 × 10−11) is 54 kb upstream of VIP (Supplementary Fig. 4), a key neuropeptide in the SCN (ref. 19). Its intracerebroventricular administration was found to prolong rapid eye movement sleep in rabbits20. rs55694368 (P =3.9 × 10−11) is 120 kb upstream of PER2 (Supplementary Fig. 5), which has been associated with human familial advanced sleep phase syndrome7. This SNP is located in a DNAse hypersensitive site (DHS) for five cell types, including pancreas adenocarcinoma, B-lymphocyte (GM12891 and GM12892), medulloblastoma and CD4+ cells (Supplementary Table 16B), and alters five regulatory motifs. (See details in Supplementary Tables 16 and 17). rs35833281 (P=3.7 × 10−9) is 18 kb downstream of HCRTR2, or orexin receptor type 2 (Supplementary Fig. 6) and alters eight regulatory motifs (Supplementary Table 16). Mutations in HCRTR2 have been linked to narcolepsy in dogs and humans21,22. This SNP rs35833281 is in partial LD with two SNPs (r2=0.25 for rs2653349 and r2=0.31 for rs3122169) on HCRTR2 that were suggested to associate with cluster headache and narcolepsy23. These SNPs were also but less significantly associated with morningness (P=3.6 × 10−7 for rs2653349 and P=1.8 × 10−6 for rs3122169). rs11545787 (P=1.4 × 10−8) is a 3′UTR variant of RASD1 (Supplementary Fig. 7), a G protein signaling activator24 and is a promoter histone mark for six cell types (H1, umbilical vein endothelial, B-lymphocyte, lung fibroblasts, skeletal muscle myoblasts and epidermal keratinocyte), in a DHS for seven cell types (skeletal muscle myoblasts, fibroblast, hepatocytes, medulloblastoma, epidermal melanocytes, pancreatic islets and fibroblasts) (Supplementary Table 16). In fact, deletion of RASD1 has been shown to result in a reduction of photic entrainment in mouse25. rs11121022 (P=2.0 × 10−8), known to alter three regulatory motifs, is 8 kb downstream of PER3 (Supplementary Fig. 8), which affects the sensitivity of the circadian system to light26 and is involved in sleep/wake activity27. Variation in PER3 has also been associated with delayed sleep syndrome and extreme diurnal preference28. A recent smaller study13 identified another SNP (rs228697) as a significant association with diurnal preference; however, this SNP is much less significant in our GWAS (P=5.3 × 10−5) and is in low LD with our index SNP rs11121022 (r2=0.08). rs9565309 (P=3.5 × 10−8), locating in a DHS for 16 cell types (Supplementary Table 16, Supplementary Data 2), is an intronic variant of CLN5 and is ∼2 kb downstream of FBXL3 (Supplementary Fig. 9), part of the F-box protein family, which ubiquitinates light-sensitive cryptochrome proteins CRY1 and CRY2, and mediates their degradation29. Mutant FBXL3 mice were shown to have an extended circadian period30.

We found four additional SNPs are linked to genes that are plausibly circadian by literature review for reported potential connections between the genes and circadian rhythms. rs1595824 (P=1.2 × 10−10) is an intronic variant of PLCL1 (Supplementary Fig. 10), which is expressed predominantly in the central nervous system and binds to the γ-aminobutyric acid (GABA) type A receptor. rs12965577 (P=2.1 × 10−8) is an intronic variant of NOL4 (Supplementary Fig. 13), one of 20 genes with the most significant changes in expression in mice with a knock-in mutation in the α1 subunit of the GABA(A) receptor31. As most SCN neuropeptides are colocalized with GABA (ref. 32) and most SCN neurons have GABAergic synapses33, it is possible that PLCL1 and NOL4 have circadian roles. rs34714364 (P=2.0 × 10−10), an enhancer histone mark, known to alter 11 regulatory motifs, a synonymous variant of gene CA14, is 3 kb away from APH1A (Supplementary Fig. 11). APH1A encodes a component of the γ-secretase complex which cleaves the β-amyloid precursor protein34, and is regulated by orexin and the sleep-wake cycle35. This relationship of γ-secretase and sleep-wake cycle suggests a circadian role for APH1A, but this region has many genes and further work is needed to verify this hypothesis. rs3972456 (P=6.0 × 10−9), locating in a DHS for 8 cell types and known to alter three regulatory motifs, is an intronic variant of FAM185A and is 16 kb away from FBXL13 (Supplementary Fig. 12). FBXL13 also encodes a protein-ubiquitin ligase and may have a circadian role similar to FBXL3.

The relationship of the remaining loci to circadian rhythm is less clear. rs12927162 (P=1.6 × 10−12) is 104 kb upstream of TOX3 (Supplementary Fig. 14), a gene associated with restless leg syndrome36. The regional plot around rs12927162 shows that the next best SNP only has a P value of 10−6. This SNP alters a POU2F2 motif, but we found no other functional annotation, and additional work is needed to verify this association. Notably, this SNP is not in LD (r2 =1.2 × 10−4) with the reported SNP rs3104767 for restless leg syndrome36 and SNPs rs3803662 and rs4784227 for breast cancer37,38 (Supplementary Table 12). And none of these SNPs have strong association with morningness (P>0.01). rs10493596 (P =8.0 × 10−12) is 21 kb upstream of AK5 (Supplementary Fig. 15), a gene that regulates adenine nucleotide metabolism expressed only in the brain39. rs2948276 (P=1.1 × 10−8, Supplementary Fig. 16), known to locate in a DHS for three cell types and alter four motifs, is 192 kb downstream of DLX5 and 118 kb upstream of SHFM1, a region linked to split hand/foot malformation. rs6582618 (P=1.5 × 10−8) is 2 kb upstream of ALG10B (Supplementary Fig. 17), a gene with a role in regulation of cardiac rhythms40.

For the above significant loci, we performed stepwise conditional analyses to identify potential additional associated variants that are within 200 kb of the index SNPs. We iteratively added new SNPs into the model until no SNP had P<1.0 × 10−5. We identified one new SNP (Supplementary Table 6) respectively for the locus close to VIP (rs62436127, P=1.6 × 10−6), APH1A (rs10888576, P=5.0 × 10−6) and PER2 (rs114769095, P=9.7 × 10−6). Accounting for the ∼15,000 total SNPs that we included in our conditional analysis, the secondary hit around VIP is significant (P<3.3 × 10−6) but the other two are not.

We tested for interaction between these SNPs and age, gender, BMI, alcohol abuse, nicotine abuse and current caffeine use (see Supplementary Table 1 for definitions). First, we added each covariate into the null model of morning person versus age, sex and five PCs. Effects of BMI (OR=0.97 kg−1 m−2, P=1.0 × 10−125) and nicotine abuse (OR=0.71, P=3.9 × 10−41) were significant (Supplementary Table 7A). We then added each SNP into each new null model. Effect sizes were not substantially altered, though P values generally became less significant, consistent with the degree of reduction in sample size for these covariates (Supplementary Table 7B). We also added interaction terms (Supplementary Table 7C) for the significant SNPs and covariates to each model and found none that would be significant after accounting for multiple testing. In addition, we estimated SNP effects in three age groups (<45, 45–60 and >60) and found them consistent across these groups (P>0.01, Supplementary Table 7D). We also estimated 21% (95% confidence interval (CI; 13%, 29%)) of the variance of the liability of morningness can be explained by genotyped SNPs, using Genome-wide Complex Trait Analysis (GCTA) (ref. 41) on a random subset of 10,000 samples due to computational constraints. Finally, we included the ‘neutral’ responders and defined a chronotype phenotype to describe morning, neutral and night person and then performed GWAS on it using a linear model with adjustment of age, sex and top five PCs. We found the results are largely similar to our morning-person GWAS. Detailed comparison (Supplementary Fig. 18) shows that in the chronotype GWAS the loci near FBXL3, RASD1 and NOL4 were no longer genome-wide significant. Two additional loci reached genome-wide significance at rs2975734 in MSRA (Supplementary Fig. 19) and rs9357620 in PHACTR1 (Supplementary Fig. 20, Supplementary Table 10). MSRA has been related to circadian rhythms in Drosophila42. PHACTR1 has not been reported to relate to circadian rhythms but has known associations with myocardial infarction43.

Pathway analyses

We used MAGENTA (ref. 44) to evaluate whether any biological pathways were enriched in our GWAS results (Table 3). The top three pathways are circadian related and share four genes: PER2 (gene based P value=1.6 × 10−8), ARNTL (P=1.2 × 10−3), CRY1 (P=3.7 × 10−3) and CRY2 (P=5.2 × 10−3). In addition, PER3 (P=1.4 × 10−7), in the KEGG circadian rhythm pathway, and FBXL3 (P=9.4 × 10−8), in the REACTOME circadian clock pathway, have strong effects and were implicated in our GWAS. Other circadian genes also contribute to the enrichment of circadian pathways, but less significantly (Table 3). The BH4 related pathway (gene set P=3.1 × 10−3) has a major role in the biosynthesis of melatonin, serotonin and dopamine, which are important hormones involved in circadian rhythm regulation and brain function. The phospholipase C (PLC) β-mediated events pathway (P=4.3 × 10−3) includes GNAO1 (P=6.2 × 10−4), GNAI3 (P=5.5 × 10−3), GNAT1 (P=1.0 × 10−2) and many other G protein related genes involved in visual phototransduction. GNAT1 is related to night blindness45 and GNAI3 is known to interact with RGS16 (ref. 46). Interestingly, RGS16 is close to our GWAS top hit. This pathway also includes PRKAR2A (P=1.4 × 10−3) and PRKACG (P=0.047), which relate to cAMP dependent protein kinase A, known to regulate critical processes in the circadian negative feedback loops47. Notably, except for the KEGG circadian rhythm pathway, which has a false discovery rate 0.06, all other associated pathways have false discovery rate >0.2, meaning the statistical evidence of the association is not strong.

Table 3 Top five morningness-associated pathways analysed by MAGENTA. Full size table

We assessed correlations between morningness and related phenotypes with adjustment for potential confounders by regression with covariates for age, gender and ancestry (Table 4). The covariate-adjusted odds of having insomnia for morning people is 55% of that for night people (P=1.5 × 10−140) and the adjusted odds of having sleep apnoea for morning people is 64% of that for night people (P=4.0 × 10−54). Morning people are also less likely to require >8 h of sleep (OR=0.69, P=6.3 × 10−53), to sleep soundly (OR=0.81, P=6.8 × 10−24), to have restless leg syndrome (OR=0.71, P=4.1 × 10−15) and sweat while sleeping (OR=0.90, P=7.9 × 10−6) after adjusting for covariates. Sleepwalking and actual amount of sleep do not correlate with morningness (P>0.1) in the full model. These associations are consistent with previous studies of insomnia48, sleep apnoea49 and sleep needed50. We calculated the association between the 15 GWAS identified SNPs and these eight sleep phenotypes (Supplementary Table 4) but found no significant associations. In addition, we looked up the SNPs and their proxies in the latest BMI GWAS from the GIANT consortium51 and the latest major depressive disorder GWAS from the CONVERGE Consortium52. But we did not find significant associations (Supplementary Table 4B,C).

Table 4 Association of morningness and other phenotypes adjusting for age, sex, and 5 PC. Full size table

We examined previously identified associations of morningness with BMI (ref. 5) and depression6. We found that that the covariate-adjusted odds for morning people to report depression is 61% of that for night people (P=3.5 × 10−138), and the average BMI for morning people is 0.99 kg m−2 lower (P=1.6 × 10−125), adjusting for covariates (Table 4). We also calculated the association between the 15 significant GWAS SNPs and depression and BMI but found no significant associations (Supplementary Table 4).

MR analyses

We used a MR approach to find evidence in support of a causal relationship of morningness with BMI. We first calculated a morningness genetic risk score by summing the risk alleles of the seven circadian related SNPs weighted by their effects, then regressed morningness or BMI against this instrument variable while adjusting for covariates (age, sex and top five PCs), and consequently estimated the ratio of the covariate-adjusted genetic effect for morningness to that for BMI (Methods section). Morningness is highly correlated (F statistic=19.0, P=2.1 × 10−80 in the linear regression model and P=1.5 × 10−79 in the logistic regression) with the genetic risk, but BMI (P=0.43) is not (Table 5). We further estimated the transferred genetic effect, that is, the effect from genetically elevated chance of being a morning person on BMI as −0.34 kg m−2(95% CI: (−0.99, 0.96), P=0.91) per unit increase of probability of being a morning person. Similarly, we found that depression is not significantly correlated with morningness genetic risk (P=0.10). We estimated a non-significant transferred genetic effect of morningness on depression: the probability of depression decreases by 0.07 (95% CI: (−0.10, 0.11), P=0.18) per unit increase of probability of being a morning person. Thus, we did not find evidence for morningness to be protective of depression or high BMI. Notably, the power of the MR analysis is governed by the strength of the correlation between morningness and its genetic risk as well as the magnitude of the transferred genetic effect of morningness on BMI or depression. We ran simulations (Methods section) to assess the power for our MR and found that our current sample sizes, though large by conventional standards, only lead to moderate power in our MR analysis of morningness and BMI and depression (Supplementary Table 8). If the observed correlation is entirely causal, our analysis has only ∼40% power. Our reported lack of statistical evidence in our MR analysis could be due to constrained study power.

Table 5 The relationship between morning person status and BMI and depression, adjusting for covariates. Full size table

We also conducted an MR analysis of BMI on morningness. We retrieved the morningness GWAS results for a set of 28 previously reported BMI associated SNPs (ref. 53) and found rs1558902, an intronic variant of FTO, had some evidence for association with morningness (P=6.0 × 10−6, Supplementary Table 5). We then calculated a BMI genetic risk with this set of SNPs using the previously reported effect sizes. It is highly correlated with BMI (F statistic=47.4, P<1.0 × 10−200) but we found it to be uncorrelated with morningness (P=0.26) and found no support for a causal relationship (transferred genetic effect=0.0029, 95% CI: (−0.0059, 0.006), P=0.35). Our power calculation (Supplementary Table 8) shows that this MR analysis is well powered (∼80%) to show evidence of a causal relationship between BMI and morningness, assuming the observed correlation is entirely causal.