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. 2022 Sep 22;18(9):e1010356. doi: 10.1371/journal.pgen.1010356

The impact of Mendelian sleep and circadian genetic variants in a population setting

Michael N Weedon 1,*, Samuel E Jones 1,2, Jacqueline M Lane 3,4,5, Jiwon Lee 6, Hanna M Ollila 2,7,8,9, Amy Dawes 1, Jess Tyrrell 1, Robin N Beaumont 1, Timo Partonen 10, Ilona Merikanto 10,11, Stephen S Rich 12, Jerome I Rotter 13,14, Timothy M Frayling 1, Martin K Rutter 15,16, Susan Redline 6, Tamar Sofer 6, Richa Saxena 3,4,5, Andrew R Wood 1,*
Editor: Gregory S Barsh17
PMCID: PMC9499244  PMID: 36137075

Abstract

Rare variants in ten genes have been reported to cause Mendelian sleep conditions characterised by extreme sleep duration or timing. These include familial natural short sleep (ADRB1, DEC2/BHLHE41, GRM1 and NPSR1), advanced sleep phase (PER2, PER3, CRY2, CSNK1D and TIMELESS) and delayed sleep phase (CRY1). The association of variants in these genes with extreme sleep conditions were usually based on clinically ascertained families, and their effects when identified in the population are unknown. We aimed to determine the effects of these variants on sleep traits in large population-based cohorts. We performed genetic association analysis of variants previously reported to be causal for Mendelian sleep and circadian conditions. Analyses were performed using 191,929 individuals with data on sleep and whole-exome or genome-sequence data from 4 population-based studies: UK Biobank, FINRISK, Health-2000-2001, and the Multi-Ethnic Study of Atherosclerosis (MESA). We identified sleep disorders from self-report, hospital and primary care data. We estimated sleep duration and timing measures from self-report and accelerometery data. We identified carriers for 10 out of 12 previously reported pathogenic variants for 8 of the 10 genes. They ranged in frequency from 1 individual with the variant in CSNK1D to 1,574 individuals with a reported variant in the PER3 gene in the UK Biobank. No carriers for variants reported in NPSR1 or PER2 were identified. We found no association between variants analyzed and extreme sleep or circadian phenotypes. Using sleep timing as a proxy measure for sleep phase, only PER3 and CRY1 variants demonstrated association with earlier and later sleep timing, respectively; however, the magnitude of effect was smaller than previously reported (sleep midpoint ~7 mins earlier and ~5 mins later, respectively). We also performed burden tests of protein truncating (PTVs) or rare missense variants for the 10 genes. Only PTVs in PER2 and PER3 were associated with a relevant trait (for example, 64 individuals with a PTV in PER2 had an odds ratio of 4.4 for being “definitely a morning person”, P = 4x10-8; and had a 57-minute earlier midpoint sleep, P = 5x10-7). Our results indicate that previously reported variants for Mendelian sleep and circadian conditions are often not highly penetrant when ascertained incidentally from the general population.

Author summary

Clinically ascertained family-based studies have previously identified rare genetic variation associated with causing life-long sleep conditions, specifically shorter sleep, and earlier or later sleep timing. However, the effects of previously reported genetic variants on sleep duration and timing when identified incidentally through population-based studies are not known. Here, we take advantage of up to 191,929 individuals from four population-based studies, including the UK Biobank, to estimate the effects of these variants on sleep duration and timing using self-reported and accelerometer-based sleep estimates coupled with sequencing data. Our analysis revealed no association between variants previously reported and extreme sleep conditions. Two variants located in two genes (PER3 and CRY1) showed evidence of association with sleep timing, but their estimated effects (~5 to 7 minutes) on sleep timing are much smaller relative to those previously reported. Our results indicate that previously reported variants are not causal for extreme sleep conditions in the general population. Finally, although we were unable to analyse a previously reported variant in the PER2 gene associated with sleep timing, additional analysis in the UK Biobank revealed carries of protein-truncating variants in this gene have an approximately 1-hour earlier sleep midpoint compared to non-carriers. These population-based estimates are important because of the recent dramatic increase in direct-to-consumer and health service genome-wide genetic testing.

Introduction

Rare variants in ten genes have been reported to cause Mendelian sleep conditions that are characterised by extreme sleep duration or timing. For example, variants in the ADRB1, NPSR1 and GRM1 genes have been recently reported to cause familial natural short sleep among carriers, defined as 4 to 6 hours sleep with no adverse effects on mental health or well-being[1–3]. Short sleep duration has also been reported to be caused by variants in the DEC2/BHLHE41 gene, a well-known Mendelian sleep gene[4]. Familial advanced sleep phase (FASP) where sleep timing is shifted 3 or 4 hours earlier has been reported for variants in PER2[5], PER3[6], CRY2[7], CSNK1D[8] and TIMELESS[9]. The opposite condition, familial delayed sleep phase disorder has been reported to be caused by a gain-of-function CRY1 variant, c.1657+3A>C, with affected individuals sleeping approximately 1 hour later than unaffected individuals[10].

The effect of variants in these genes on sleep duration and sleep timing when identified incidentally in the general population is unknown. Discovery efforts for these variants generally used either a single pedigree or a small number of families selected on a specific clinical phenotype. For example, for ADRB1, six individuals in a single family were affected with familial natural short sleep. This “phenotype first” method of discovery means we do not know the effect of these variants when identified in an individual from the general population (i.e. from a “genotype first” approach). It is important to re-evaluate effect estimates to understand the underlying biology which may inform clinical risk stratification, and because of the recent dramatic increase in direct-to-consumer (DTC) and health service genome-wide genetic testing. To assess the effect of these variants when identified incidentally, large, unselected population cohorts are needed.

Estimating the effects of these variants in the general population has not previously been possible due to limitations in the availability of genetic data coupled with sleep parameters. The UK Biobank, a population-based study of 500,000 individuals from the UK, provides an opportunity to address questions of pathogenicity and penetrance of rare genetic conditions[11]. We have previously shown, for a range of traits and diseases, that disease penetrance is generally lower in UK Biobank compared to that reported from clinical cohorts[12,13]. For example, using activity monitor derived and self-report estimates of sleep timing from the UK Biobank, we have demonstrated that the effect of the PER3 P415A/H417R familial advanced sleep phase variant on sleep timing is substantially lower than the published estimate (0.13hrs vs. 4.2hrs)[14]. However, our previous studies were based on genotyping array data of relatively common single nucleotide polymorphisms (SNPs). Genotyping arrays are known to capture a relatively small number of rare coding pathogenic disease variants, typically with poor accuracy for genotyping and imputation [13,15,16].

In this study, we use exome sequencing data in up to 184,065 individuals of European ancestry from the UK Biobank (October 2020 release) with sleep data, with additional data from up to 2,015 individuals from the Multi-Ethnic Study of Atherosclerosis (MESA), and 5,929 individuals from the FINRISK and Health 2000–2011 studies to comprehensively assess the penetrance of Mendelian sleep and circadian genes in a population-based setting. We show that previously reported variants for Mendelian sleep and circadian conditions are often not highly penetrant when identified incidentally from the general population.

Methods

Ethics statement

The UK Biobank was granted ethical approval by the North West Multi-centre Research Ethics Committee (MREC) to collect and distribute data and samples from the participants (http://www.ukbiobank.ac.uk/ethics/) and covers the work in this study, which was performed under UK Biobank application numbers 9072 and 16434. All participants included in these analyses gave written consent to participate.

UK Biobank study participants

The primary study population was drawn from the UK Biobank study–a longitudinal population-based study of individuals residing in the UK. We restricted our analysis to a subset of 184,532 Europeans with whole-exome sequence data, including 170,518 unrelated Europeans (<3rd degree) defined through kinship coefficients made available from the UK Biobank. Details on derivation of genetic ancestry has previously been reported in Jones et al.[14].

Exome sequence data in UK Biobank

We used the second release of exome-sequence data from the UK Biobank (October 2020). Specifically, we used genotypes called and provided in binary PLINK format (data field: 23155). Genotypes for previously reported Mendelian causes of sleep and circadian conditions were extracted for subsequent data analysis. Details of central processing of whole-exome data on 200K UK Biobank participants can be found online as part of UK Biobank’s data showcase: https://biobank.ctsu.ox.ac.uk/crystal/label.cgi?id=170. All variants passed central quality control[17]. Sequence data for variants analysed in this paper were manually inspected through IGV[18] plots.

Sleep phenotypes and variant selection

We focussed our analysis on phenotypes previously reported to have Mendelian causes, specifically familial natural short sleep (≤6 hours)[1,3,4], familial advanced sleep phase characterised by earlier sleep onset and earlier waking[5–9], and delayed sleep phase disorder associated with later sleep onset and later waking[10].

Sleep disorders and medication

We used data from the UK Biobank variable 131061 (https://biobank.ndph.ox.ac.uk/showcase/field.cgi?id=131061) which classifies individuals as having a sleep disorder based on self-report, hospital and primary care data. As this variable does not separate out sub classifications of sleep disorders we used ICD-10 codes G472 and F512 from in-patient data to specifically assess disorders of the sleep wake cycle. Medication use at baseline in the UK Biobank was identified from variable 20003 (https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=20003). Classification of sleep medication used in this paper has previously been described[19]. Since ‘sleep’ is a behavior, phenotyping is complicated. There are extreme sleep patterns like ASP and NSS which are often not considered ‘disorders’ by individuals if the trait does not interfere with an individual’s work and social demands[20].

Sleep duration

We used self-reported sleep duration from UK Biobank questionnaire data (data field: 1160). We excluded individuals who reported >12 hours sleep duration, did not know or preferred not to answer. This data was also dichotomized for multiple analyses to define “short sleepers” as individuals self-reporting sleep duration ≤6 hours, ≤5 hours, ≤4 hours, and ≥4 hours ≤6. A maximum of 166,360 individuals had genotype and self-report sleep duration available across the variants previously reported to be Mendelian causes of familial natural short sleep and prioritised for analysis. In addition, we used accelerometer estimates of nocturnal sleep derived in a previous study[21]. A maximum subset of 34,241 individuals remained for analysis of accelerometer-based sleep duration estimates after removing individuals (n = 4,323) with problematic accelerometer data processing or who were outliers for the number of nocturnal sleep episodes used to derive nocturnal sleep estimates[21]. There was no association for individual variants or overall with the individuals removed from the accelerometer analyses. We also applied additional quality control among non-carriers of previously reported variants described in this study by removing individuals with >12 hours estimated sleep duration.

Sleep timing

We used self-reported chronotype (diurnal preference) available in the UK Biobank (data field: 1180) as a proxy for sleep timing, whereby we assumed morning people to sleep earlier and evening people to sleep later. We created four binary variables to represent chronotype where individuals were coded ‘1’ based on being: 1) definitely a morning person; 2) more or definitely a morning person; 3) definitely an evening person, and 4) more or definitely an evening person. For each variable, individuals who did not report having the respective circadian preference(s) (including “Do not know” but excluding “Prefer not to answer”) were coded ‘0’. A maximum of 168,409 individuals had genotype data and self-report chronotype data across the variants associated with advanced and delayed sleep phase. In addition, we used accelerometer estimates from up to 7 nights of the least-active 5 hours (L5) over a 24-hour period, with values representing hours from the previous midnight (e.g. 7 p.m.  =  19 and 2 a.m.  =  26)[21]. Sleep midpoint was estimated as the mid-point of the sleep period time window used to define sleep duration[21]. In total, a maximum subset of 34,650 individuals remained for statistical analysis of accelerometer-based sleep timing estimates. We applied additional quality control among non-carriers of previously reported variants described by removing individuals outside 4 standard deviations of the respective trait (midpoint sleep or L5 timing) analysed.

Replication of findings

To replicate our UK Biobank observations of previously reported variant-phenotype associations, we used self-reported measures of sleep duration and circadian preference (chronotype) in up to 5,929 individuals from two population-based studies from Finland: FINRISK[22] and Health-2000-2011 (https://www.julkari.fi/handle/10024/130780) and publicly available summary statistics from release 6 of FinnGen (https://r6.finngen.fi/). In addition, we used accelerometer-based estimates of sleep timing (sleep midpoint) in up to 1,935 individuals from the Multi-Ethnic Study of Atherosclerosis (MESA) Sleep Study[23] where sequence-based genotypes of previously reported variants associated with sleep timing from its Exam 5 were available (see S1 Methods for study descriptors).

Statistical Analysis

Defining genotype groups for comparison of sleep parameter estimates

For each variant, summaries of sleep parameter estimates were analysed by genotype. The set of individuals classified as homozygous reference for all variants was the same after removing individuals from this genotype group who were carriers for any previously reported variant.

Analysis of self-report phenotypes

t-tests were performed to compare means and standard deviations of the continuous self-report sleep duration variables across genotype groups and carrier status, respectively. For dichotomized variables, Fisher’s exact tests were performed to compare proportions of individuals labelled as short sleepers or had a defined circadian preference (above) across genotype groups. Alternate homozygous counts were combined with heterozygous counts when performing Fisher’s exact test where applicable. In addition, logistic regression was performed using UK Biobank data to obtain odds ratios, adjusting for age at baseline (field 21003), sex (field 31), assessment centre (field 54), month when attending the assessment centre (field 55), and 40 genetic principal components (field 22009) available from the UK Biobank.

Accelerometer-derived phenotypes

t-tests were performed to compare means and standard deviations of the accelerometer-based estimates of sleep duration, sleep-midpoint and L5 timing.

Burden testing of rare loss-of-function and missense variants in UK Biobank

Genetic variants identified in the ten previously reported genes were annotated using the Ensembl Variant Effect Predictor (VEP)[24] and LOFTEE[25]. Variants with MAF<0.0001 and annotated as missense or loss-of-function with high confidence were analysed through burden testing as implemented in REGENIE[26] that accounts for relatedness among individuals analysed. We analysed up to 184,065 individuals of inferred European genetic ancestry with available sleep and covariate data.

Results

Most reported Mendelian sleep variants are present in UK Biobank and are not associated with sleep disorders

We assessed the frequencies of 12 variants in 10 genes that have been reported to cause familial natural short sleep, familial advanced sleep phase, or delayed sleep phase conditions (S1 Table). All were present in the UK Biobank in unrelated individuals of European ancestry except for the S662G variant in PER2 (not present in gnomAD (version 2.1)) and the Y206H variant in NPSR1. In addition, we identified carriers for previously reported variants in GRM1, BHLHE41/DEC2, CRY1, and PER3 in the MESA and Finnish studies (S2 Table). None of the variants were associated with any self-report or clinically diagnosed sleep disorder in UK Biobank, including the G472 or F512 ICD-10 code for disorders of the sleep wake cycle or with sleep medication use (Table 1). We noted that in release 6 of the FinnGen study there was a nominal association with ICD10 code G472 (circadian rhythm sleep disorders) for the CRY1 c.1657+3A>C variant (0.05% in controls vs. 0.29% in cases, P = 0.026), but this variant is 10-fold rarer in the Finnish population than in non-Finnish Europeans and is imputed with a quality score of only 0.76.

Table 1. Sleep disorder and medication status by carrier status of variants previously reported to affect sleep duration or timing.

Sleep disorders in the UK Biobank coded as G47 from self-report, ICD-10 codes or primary care data. Also presented are results for disorders of the sleep wake cycle (ICD10: G472, F512). Sleep medication codes reported at UK Biobank baseline at reported in Lane et al. Nature Genetics, 2019.

Any self-report, primary care or ICD-10 code sleep disorder record (G47) Any sleep medication Any ICD-10 code for sleep-wake
disorder (G472 or F512)
Gene Variant Genotype Controls N Cases N Cases% Pa No sleep
Meds N		 Sleep
Meds N		 Sleep
Meds %		 Pa Controls
N		 Cases
N		 Cases
%		 Pa
ADRB1 A187V C/C 164,526 5,921 3.47 0.310 155,977 14,467 8.49 0.386 170,420 27 0.02 1.000
C/T 66 4 5.71 62 8 11.43 70 0 0.00
DEC2/BHLHE41 P384R G/G 164,577 5,921 3.47 0.298 156,023 14,472 8.49 0.588 170,471 27 0.02 1.000
G/C 9 1 10.00 9 1 10.00 10 0 0.00
GRM1 S458A T/T 164,526 5,923 3.47 1.000 155,976 14,470 8.49 1.000 170,422 27 0.02 1.000
T/G 65 2 2.99 62 5 7.46 67 0 0.00
A889T A/A 164,586 5,925 3.47 1.000 156,033 14,475 8.49 1.000 170,484 27 0.02 1.000
A/T 3 0 0.00 3 0 0.00 3 0 0.00
PER3 P415A C/C 163,079 5,862 3.47 0.240 154,589 14,348 8.49 0.413 168,914 27 0.02 1.000
C/G 1,505 62 3.96 1,442 123 7.86 1,567 0 0.00
G/G 6 1 14.29 6 1 14.29 7 0 0.00
H417R A/A 163,078 5,862 3.47 0.268 154,589 14,348 8.49 0.525 168,913 27 0.02 1.000
A/G 1,507 62 3.95 1,444 125 7.97 1,569 0 0.00
G/G 6 1 14.29 6 1 14.29 7 0 0.00
CRY2 A260T G/G 164,555 5,924 3.47 1.000 156,008 14,468 8.49 0.044 170,452 27 0.02 1.000
G/A 38 1 5.71 32 7 17.95 39 0 0.00
TIMELESS R1081X G/G 164,580 5,925 3.47 1.000 156,027 14,475 8.49 1.000 170,478 27 0.02 1.000
G/A 6 0 5.71 6 0 0.00 6 0 0.00
CRY1 c.1657+3A>C T/T 163,148 5,879 3.47 0.477 154,669 14,355 8.49 0.575 169,000 27 0.02 1.000
T/G 1,435 46 3.47 1,362 119 8.04 1,481 0 0.00
G/G 9 0 5.71 8 1 11.11 9 0 0.00
CSNK1D T44A T/T 164,592 5,925 3.47 1.000 156,039 14,475 8.49 1.000 170,490 27 0.02 1.000
T/C 1 0 5.71 1 0 0.00 1 0 0.00
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aP-value derived from 2-sided Fisher’s exact test. Homozygous carriers for minor alleles were combined with heterozygous carriers prior to performing Fisher’s exact test.

ADRB1, GRM1, or DEC2/BHLHE41 pathogenic variants are not associated with self-reported short sleep duration in population-based cohorts

We identified 149 unrelated individuals from the UK Biobank with self-reported measures of sleep duration and carrying a previously reported pathogenic variant for natural short sleep in ADRB1 (A817V, n = 69), DEC2/BHLHE41 (P384R, n = 10), or GRM1 (S458A, n = 67; A889T, n = 3). We found no evidence that these individuals have short sleep durations in the UK Biobank (Tables 2, S3 and S4). For example, the 69 carriers of the ADRB1 variant had a self-reported average sleep duration of 7.1 hours (95% CI: 6.9, 7.3) compared to 7.2 hours (95% CI: 7.19, 7.21) among non-variant carriers (t-test P = 0.62). The proportion of ADRB1 variant carriers self-reporting ≤6 hours of sleep, 23.2%, was no different to the proportion of people not carrying the variant, 23.7% (Fisher’s exact P = 1.00). Using a fully adjusted logistic regression model gave similar results with an odds ratio of 0.98 (95% CI: 0.56–1.72; P = 0.96) for sleeping ≤ 6 hours. We observed zero carriers of the ADRB1 variant self-reporting a more extreme phenotype of ≤4 hours of sleep. Similar observations were made for previously reported monogenic sleep disruption variants in DEC2/BHLHE41 and GRM1. The 10 carriers of the P384R variant in the DEC2/BHLHE41 gene had an average self-reported sleep duration of 7.3 hours compared to 7.2 hours among non-carriers (P = 0.69), with 10% of carriers reporting ≤6 hours sleep duration compared to 23.7% among non-carriers of reported variants (P = 0.47). Carriers for variants in the GRM1 gene did not significantly differ in sleep duration from non-carriers (S458A: 7.1 hours (carriers) vs 7.2 hours (non-carriers), P = 0.81; A889T: 8.0 hours (carriers) vs 7.2 hours (non-carriers), P = 0.18), or the number of individuals reporting sleep duration of ≤6 hours (S458A: 28.4% (carriers) vs 23.7% (non-carriers), P = 0.39; A889T: 0% (carriers) vs 23.8% (non-carriers), P = 1.00).The null effect of the S458A variant in GRM1 was also observed in FINRISK/Health 2000–2011 where <5 S458A carriers were identified and had no statistically significant difference compared to non-carriers in average sleep duration (t-test P = 0.92) or proportion self-reporting ≤6 hours sleep (Fisher’s exact P = 1.00) (Table 2).

Table 2. Summary of self-reported sleep duration in UK Biobank (data field 1160) between carriers of variants previously reported to be causal for familial natural short sleep and non-carriers (homozygous reference called by UK Biobank exome-sequencing) who are also non-carriers for any of the other 12 variants described in this article.

Self-report sleep duration (hrs) Reporting ≤6 hours sleep
Gene Variant REF/ALTa Study Genotype Nb Minc Maxd Mean (SDe) Pf %g Cases / Controls Ph
ADRB1 A187V C/T UKB C/C 166,291 1 12 7.17 (1.07) 0.62 23.74 39,484 / 126,807 1.00
C/T 69 5 12 7.10 (1.03) 23.19 16 / 53
DEC2/BHLHE41 P384R G/C UKB G/G 166,283 1 12 7.17 (1.07) 0.69 23.74 39,483 / 126,800 0.47
G/C 10 5 9 7.30 (1.06) 10.00 1 / 9
GRM1 S458A T/G UKB T/T 166,290 1 12 7.17 (1.07) 0.81 23.74 39,484 / 126,806 0.39
T/G 67 4 10 7.13 (1.15) 28.36 19 / 48
FINRISK/Health 2000–2011 T/T 5,927 3 15 7.59 (1.20) 0.92 14.10 837 / 5,090 1.00
T/G <5 7 8 7.50 (0.71) 0.00 0 / <5
A889T A/T UKB A/A 166,288 1 12 7.17 (1.07) 0.18 23.75 39,485 / 126,803 1.00
A/T 3 8 8 8.00 (0.00) 0.00 0 / 3
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areference allele / alternate allele

bnumber of individuals in genotype group

cminimum sleep duration self-reported in genotype group

dmaximum sleep duration self-reported in genotype group

estandard deviation

ft-test P-value (two-sided)

g% = percentage of individuals in genotype group self-reporting ≤6 hours sleep duration (cases)

hFisher’s exact-test P-value (two-sided).

ADBR1, GRM1, or DEC2/BHLHE41 pathogenic variants are not associated with accelerometer derived measures of sleep in population-based cohorts

We confirmed the lack of association between previously identified pathogenic variants and sleep duration using accelerometer estimates of sleep in a subset of 34,226 individuals from the UK Biobank. Fifteen ADRB1 variant carriers with accelerometer-derived sleep estimates had an average sleep duration of 7.6 hours (95% CI: 7.4, 7.8) compared to 7.3 hours (95% CI: 7.29, 7.31) among non-variant carriers (t-test P = 0.20) (Table 3). All 15 ADRB1 variant carriers had an accelerometer-based sleep duration average of more than 6 hours (min = 6 hours, 53 minutes). Similar observations were made when stratifying accelerometer data analyses to weekend nights and weekday nights (S5 Table).

Table 3. Summary of average accelerometer derived sleep duration (hours) (all nights) in the subset of exome-sequenced unrelated Europeans in UK Biobank, split by carriers and non-carriers for variants previously reported to be causal for familial natural short sleep.

Gene Variant Genotype Na Minb Maxc Mean SDd Pe
ADRB1 A187V C/C 34,168 1.63 11.87 7.30 0.86 0.20
C/T 15 6.89 8.83 7.59 0.49
DEC2/BHLHE4 P384R G/G 34,167 1.63 11.87 7.30 0.86 0.62
G/C 4 6.26 8.25 7.51 0.92
GRM1 S458A T/T 34,166 1.63 11.87 7.30 0.86 0.92
T/G 10 6.48 8.30 7.33 0.76
A889T A/A 34,167 1.63 11.87 7.30 0.86 -
A/T 0 - - - -
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anumber of individuals in genotype group

bminimum average sleep duration in genotype group

cmaximum average sleep duration in genotype group

dstandard deviation

et-test P-value (two-sided).

PER3, but not CRY2 or TIMELESS, variants are associated with advanced sleep phase in the population-based cohorts, but with reduced effect size

Variants in five genes have previously been associated with familial advanced sleep phase syndrome–characterised by approximately ≥3 hour shifts towards earlier sleep and wake times. We previously tested the PER3 P415A/H417R variant in the UK Biobank and found it was associated with chronotype and activity monitor derived sleep timing, although the size of the effect on sleep timing (L5 time) was smaller than the initially published estimate of 4.2 hours (7.8 minutes, 95% CI: 4.2–13.2 minutes, P = 4.3×10−4)[14]. We confirmed this association using exome sequence data and accelerometer data. The difference on average sleep-midpoint timing between carriers and non-carriers based on exome-sequence data was 6.8 minutes (95% CI: 1.4–12.3 minutes, P = 0.01) with a similar effect size for L5 timing (Table 4). Variant carriers had an odds ratio of 1.36 (95% CI: 1.22–1.52, P = 2×10−8) for “definitely” being a morning person. We found no evidence that carriers of previously reported pathogenic variants in the other two genes, CRY2 and TIMELESS, had altered chronotype, L5 timing or sleep-midpoint indicative of earlier sleep timing (S6–S8 Tables).

Table 4. Proportion of individuals self-reporting as being “definitely a morning person”, average L5 timing, and average of sleep-midpoint across all nights for each genotype group of variants previously reported to be causal for familial advanced sleep phase in the UK Biobank (UKB), Finnish and MESA studies.

Accelerometer-based estimates of sleep timing unavailable in the Finnish studies. Self-reported “morningness” and accelerometer estimates of L5-timing unavailable in MESA.

Definitely a "Morning" Person Accelerometer L5 Timing Accelerometer Sleep Midpoint Timing
Gene Variant Study Genotype %a Cases / Controls Pb Nc Mind Maxe Mean SDf Pg Nc Mind Maxe Mean SDf Pg
PER3 P415A UKB C/C 23.77 39,655 / 127,177 <0.001 33,998 23.08 31.51 27.32 0.99 0.053 33,908 23.37 30.59 27.01 0.85 0.014
C/G 29.58 463 / 1,102 338 21.34 31.43 27.21 1.05 338 19.45 29.64 26.90 0.96
G/G 42.86 3 / 4 1 28.01 28.01 28.01 - 1 27.60 27.60 27.60 -
FINRISK/Health 2000–2011 C/C 22.4 613 / 2,121 1.000 - - - - - - - - - - -
C/G 22.8 34 / 115 - - - - - - - - - - -
G/G 0.00 0 / <5 - - - - - - - - - -
MESA C/C - - - - - - - - - 1,925 13.15 34.92 27.05 2.16 0.900
C/G - - - - - - - 10 26.12 27.98 26.96 0.67
H417R UKB A/A 23.77 39,655 / 127,177 <0.001 33,997 23.08 31.51 27.32 0.99 0.06 33,907 23.37 30.59 27.01 0.85 0.018
A/G 29.55 463 / 1,104 339 21.34 31.43 27.21 1.05 339 19.45 29.64 26.90 0.96
G/G 42.86 3 / 4 1 28.01 28.01 28.01 - 1 27.60 27.60 27.60 -
FINRISK/Health 2000–2011 A/A 22.4 613 / 2,121 1.000 - - - - - - - - - - -
A/G 22.8 34 / 115 - - - - - - - - - - -
G/G 0.00 0 / <5 - - - - - - - - - -
MESA A/A - - - - - - - -
-		 1,925 13.15 34.92 27.05 2.16 0.900
A/G - - - - - - - 10 26.12 27.98 26.96 0.67
CRY2 A260T UKB G/G 23.77 39,655 / 127,179 0.340 33,998 23.08 31.51 27.32 0.99 0.092 33,908 23.37 30.59 27.01 0.85 0.040
G/A 15.79 6 / 32 4 27.32 28.67 28.15 0.58 4 26.63 29.34 27.88 1.14
TIMELESS R1081X UKB G/G 23.77 39,652 / 127,175 1.000 33,997 23.08 31.51 27.32 0.99 - 33,907 23.37 30.59 27.01 0.85 -
G/A 20.00 1 / 4 0 - - - - - - - - -
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apercentage of individuals in genotype group self-reporting being “definitely a ‘morning’ person”

bFisher’s exact-test P-value (two-sided)

cnumber of individuals

dminimum phenotypic value

emaximum phenotypic value

fSD = standard deviation

gcarrier status t-test P-value (two-sided). Homozygous carriers for rare alleles combined with heterozygote carriers when performing Fisher’s exact-test.

CRY1 c.1657+3A>C is associated with chronotype and a delayed sleep phase in a population-based cohort, but with reduced effect size

CRY1 c.1657+3A>C has previously been associated with delayed sleep phase disorder, characterised by an approximately 1-hour shift towards later sleep and wake times[10]. In the UK Biobank, 10.1% and 11.1% of CRY1 heterozygous and homozygous variant carriers, respectively, reported being “definitely an evening person”, compared to 7.9% of non-variant carriers (Fisher’s exact P = 0.003) (Table 5). No carriers of the CRY1 variant reported being “definitely an evening person” in the Finnish studies. The observed difference in the UK Biobank in sleep-midpoint estimated from accelerometery for individuals with a CRY1 variant was 5.4 minutes later (95% CI:-0.2,11.0, P = 0.06), with a similar difference observed for L5 timing. Similar point estimates were observed in our sensitivity analyses that included restricting accelerometer data analyses to either weekend nights or weekday nights. For example, we observed carriers of the CRY1 variant having a 6.4 minute later sleep-midpoint at weekends (95% CI, 0.4,12.4, P = 0.04) (S9–S11 Tables).

Table 5. Proportion of individuals self-reporting as being “definitely an evening person”, average L5 timing, and average sleep-midpoint across all nights for each genotype group of the CRY1 variant previously reported to be causal for delayed sleep phase disorder in the UK Biobank (UKB), Finnish and MESA studies.

Accelerometer based estimates of sleep timing unavailable in the Finnish studies. Self-reported “eveningness” and accelerometer estimates of L5-timing unavailable in MESA.

Definitely an "Evening" Person Accelerometer L5 Timing Accelerometer Sleep Midpoint
Gene Variant Study Genotype %a Cases/Controls Pb Nc Mind Maxe Mean SDf Pg Nc Mind Maxe Mean SDf Pg
CRY1 c.1657+3A>C UKB T/T 7.92 13,212 / 153,621 0.003 33,998 23.08 31.51 27.32 0.99 0.132 33,908 23.37 30.59 27.01 0.85 0.059
T/G 10.07 149 / 1,331 318 24.06 30.38 27.41 1.00 318 24.28 29.60 27.11 0.88
G/G 11.11 1 / 8 4 25.08 28.44 26.50 1.48 4 25.49 27.38 26.50 0.90
FINRISK/Health 2000–2011 T/T 10 284 / 2,554 1.000 - - - - - - - - - - - -
T/G 0 0 / <5 - - - - - - - - - -
MESA T/T - - - - - - - - - 1,914 13.19 34.92 27.05 2.14 0.568
T/G - - - - - - - 21 13.15 30.10 26.78 3.32
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apercentage of individuals in genotype group self-reporting being “definitely a ‘evening person”

bFisher’s exact-test P-value (two-sided)

cnumber of individuals.

dminimum phenotypic value

emaximum phenotypic value

fstandard deviation

gcarrier status t-test P-value (two-sided). Homozygous carriers for rare alleles combined with heterozygote carriers when performing Fisher’s exact-test.

Heterozygous protein truncating variants in reported Mendelian sleep genes PER2 and PER3 are associated with sleep timing

Most of the reported variants are missense variants and some, for example, CRY1 c.1657+3A>C have been shown to have a specific gain of function effect in in-vitro experiments[10]. Only the reported TIMELESS gene variant is a protein truncating variant (PTV) and it is unclear whether the reported Mendelian sleep variants act through loss of function due to haploinsufficiency. We therefore identified all rare (MAF<0.01%) nonsense, frameshift and essential splice site variants across the ten Mendelian sleep and circadian genes. The number of individuals with rare high confidence loss-of-function variants in these genes ranged from 10 for ADRB1 to 205 for TIMELESS. We subsequently performed burden testing of loss-of-function variants for these 10 genes in up to 184,065 individuals of European ancestry, including and adjusting for relatedness (S12–S15 Tables). We identified associations between PER2 and self-reported measures of chronotype and accelerometer-estimates of sleep timing. We observed associations between loss-of-function variants in the PER2 gene and UK Biobank participants self-reporting as “definitely a morning person” (Burden P = 4×10−8) (S13 Table), and accelerometer-estimates of sleep-midpoint (Burden P = 5×10−7) and L5 timing (Burden P = 9×10−4)(S15 Table). Of 64 unrelated European carriers carrying at least one of the 50 loss-of-function variants in the PER2 gene, 58% (n = 37) self-reported as “definitely a morning person” in contrast to 24% (n = 40,438) among non-carriers (Fisher’s exact P<0.0001) (S16 Table). Within unrelated individuals, compared with non-carriers of loss-of-function variants in PER2, carriers had an earlier average sleep-midpoint of ~57 minutes (t-test P<0.0001) (S17 Table) and an earlier average L5 timing of ~33 minutes (t-test P = 0.027) (S18 Table). In addition, our burden testing identified an association between loss-of-function variants in PER3 and L5-timing (Burden P = 4×10−6) (S15 Table). Our gene-based analyses for high confidence PTVs did not result in associations for other previously reported monogenic genes for sleep duration or timing. We observed no associations in gene-based tests of rare missense variants in these genes after accounting for multiple testing (Bonferroni P = 0.0001 based on 42 tests across 10 genes).

Discussion

Recent studies have identified 10 genes where specific variants are reported to cause familial natural short sleep, familial advanced sleep phase, or delayed sleep phase. These studies have tended to be based on a limited number of families ascertained to have a specific sleep trait. This form of ascertainment means the effect of the variants and genes when identified in the population is unknown. Here, we show that most previously reported variants for Mendelian sleep and circadian conditions are not highly penetrant when ascertained incidentally from the general population. Incidental findings are becoming increasingly common with the increase in whole genome sequencing both from direct-to-consumer companies and through health services. It is important, therefore, to get accurate estimates of the risk of developing a condition so that individuals are not misdiagnosed and/or potentially incorrectly treated for a condition.

We and others have shown previously [13] that the penetrance of rare Mendelian disease variants is likely to be lower when estimated from population-based cohorts than in ascertained discovery or clinical cohorts and this may be the case for the genes and variants reported here. It is also possible that some of the reported genes and variants are not causes of the reported sleep conditions in humans. The functional effect of each of the variants assessed here are supported by extensive in vitro and animal model studies. The level of human genetic evidence varies across studies, from 6 individuals from a single pedigree for the ADBR1 variant to 78 individuals from 7 families for the CRY1 c.1657+3A>C variant. While functional evidence in animal models is important to understand the biology of the associated variants, it is important to demonstrate robust human genetic evidence to ensure relevance in humans. It is therefore possible that some of the reported variants with weaker human genetic evidence do not cause monogenic sleep and circadian conditions in humans. However, because of the nature of the ascertainment, our study cannot address pathogenicity and can only conclude that the effect of these variants when identified incidentally from the population appears to be much weaker than previously reported.

There are several other possible explanations for the differences between our studies. Genetic background may play a role. Given the importance of sleep for survival, homeostatic mechanisms are extremely robust. For example, the very strong circadian phenotype of homozygous PER2 KO allele [27] was completely absent when crossed onto a C57/Bl6 background [28]. Our work underscores the significant challenges of behavioral genetics. It is possible that an individual or family presenting to a clinic with a specific sleep or circadian condition have an increased polygenic susceptibility to sleep duration, in addition to the monogenic variant. For example, we have recently identified 351 variants for being a morning person from genome-wide association studies. Individuals in the highest 5% of polygenic risk had an average sleep timing of 25 mins earlier compared to the lowest polygenic risk individuals. As has been shown for traits such as lipids and BMI [29], this suggests that extreme polygenic risk can have similar effect sizes to monogenic variants for sleep traits. Ancestry differences may also play a role. For example, the CRY1 c.1657+3A>C association was discovered in an American family and followed up in Turkish families. A recent paper has found association with the CRY1 variant and sleep-midpoint timing (~40 mins) in an independent cohort of Turkish ancestry individuals [30]. The difference in effect size between these studies may be due to different genetic backgrounds between these previous populations and the predominantly Northern European ancestry population used in our analyses. There are also potential environmental (e.g. daylight hours) and societal explanations for the different results in this study compared to previous studies.

There are several limitations to our study which provide other possible explanations for the weaker associations observed here compared to previous studies. First, the UK Biobank has a healthy volunteer bias [31] and may select against individuals with sleep disorders. However, this is unlikely for FASP and FNSS where the phenotypes of phase advance or short sleep rarely affect individuals’ well-being [20]. Additionally, the allele frequency of the variants in UK Biobank is similar to that in a large resource of exome data (gnomAD) suggesting limited selection against these variants. Second, reported sleep patterns that are often shaped by social factors, and thus may not reflect their underlying sleep preferences. This could explain the lack of association with sleep timing for example. However, we find no association with the variants with traits such as self-reported ease of getting up and limiting the analyses to an individual’s activity on the weekend shows a similar lack of association. Additionally, we find no association with any measures of sleep quality or disruption in the UK Biobank, including those defined by medical record codings, although sleep fragmentation has been observed in CRY1 c.1657+3A>C carriers [10].

Another limitation to this study is that it is not possible to do as detailed sleep and circadian phenotyping in this large-scale study as is possible in smaller scale clinical studies. We have, however, used multiple data sources including self-report and activity monitor data and have used primary care and inpatient data to identify sleep disorders. We have previously validated the activity monitor sleep estimates against polysomnography data [32] and it is a reliable measure of many sleep parameters. The validity of the measures is also confirmed by the statistically robust associations with PER2 and PER3 protein truncating variants using both self-report and accelerometry, with effects of sleeping timing of approximately 1 hour. We have also demonstrated this through the robust association of hundreds of common genetic variants with chronotype [14], sleep duration[33] and other sleep measures through genome-wide association studies (GWAS)[19,21]. The number of individuals with variants is relatively low for some genes, but the number of individuals carrying previously identified variants is usually larger than the number of available in the original reports.

Our work demonstrates that haploinsufficiency of PER2 affects circadian timing in humans. We find a substantial effect on chronotype and sleep timing for individuals with heterozygous PER2 protein truncating variants. This is unexpected because only homozygous Per2 knockout mice exhibit a circadian shortened circadian period, with no phenotype in heterozygotes [34]. The human genetic evidence for a role of the PER2 S662G missense variant is robust with co-segregation in a large pedigree with FASPS [5]. It was initially thought that the effect of this S662G variant was caused by decreased phosphorylation of PER by CK1ε that could stabilize it leading to PER accumulating prematurely and shortened circadian period. Others have suggested that the S662G mutation results in decreased PER2 levels and/or an increased turnover of nuclear PER2 [35]. Our work shows that, in humans, haploinsufficiency of PER2 causes a substantial effect on chronotype and sleeping timing.

Our results indicate that most previously reported variants for Mendelian sleep and circadian conditions are not highly penetrant causes of extreme sleep duration or timing when ascertained incidentally from the population.

Supporting information

S1 Table. Summary of twelve variants previously reported to be causal for Mendelian sleep and circadian conditions, including the variant frequencies catalogued in gnomAD.

(DOCX)

Click here for additional data file. (15.7KB, docx)
S2 Table. Maximum genotype counts for 12 previously reported monogenic causes of sleep and circadian conditions in unrelated individuals of European ancestry from the UK Biobank, FINRISK / Health 2000–2011, and MESA studies.

Genotype counts are based on availability of sleep characteristics relevant to each gene.

(DOCX)

Click here for additional data file. (17.1KB, docx)
S3 Table. Summary statistics of self-reported sleep duration in the UK Biobank (Field 1160) for carriers of variants previously described as causal for familial natural short sleep.

(DOCX)

Click here for additional data file. (19.9KB, docx)
S4 Table. Summary statistics of dichotomised self-reported sleep data in the UK Biobank for carriers of variants previously described as causal for familial natural short sleep.

Data unavailable for self-reported sleep of ≤5 hours, ≤4 hours and 4–6 hours in the Finnish study.

(DOCX)

Click here for additional data file. (20.9KB, docx)
S5 Table. Summary statistics of accelerometer-derived estimates of sleep duration in UK Biobank for carriers of variants previously described as casual for familial natural short sleep.

No carriers of the GRM1 A889T variant remained among individuals from UK Biobank who had worn an accelerometer.

(DOCX)

Click here for additional data file. (17.8KB, docx)
S6 Table. Summary statistics of “Morningness” across genotype groups for variants previously reported as causal for familial advanced sleep phase.

Data on being “more or definitely a morning person” unavailable in the Finnish studies.

(DOCX)

Click here for additional data file. (18.5KB, docx)
S7 Table. Summary statistics of L5-midpoint timing estimated from accelerometer data in UK Biobank across genotype groups for variants previously reported as causal for familial advanced sleep phase.

(DOCX)

Click here for additional data file. (18.2KB, docx)
S8 Table. Summary statistics of sleep-midpoint estimated from accelerometer data in UK Biobank and MESA across genotype groups for variants previously reported as causal for familial advanced sleep phase.

(DOCX)

Click here for additional data file. (20.4KB, docx)
S9 Table. Summary statistics of “eveningness” across genotype groups for variants previously reported as causal for delayed sleep phase.

Data on being “more or definitely an evening person” unavailable in the Finnish studies.

(DOCX)

Click here for additional data file. (15.4KB, docx)
S10 Table. Summary statistics of L5-midpoint timing estimated from accelerometer data in UK Biobank a across genotype groups for variants previously reported as causal for delayed sleep phase.

(DOCX)

Click here for additional data file. (15.2KB, docx)
S11 Table. Summary statistics of sleep-midpoint estimated from accelerometer data in UK Biobank and MESA across genotype groups for variants previously reported as causal for delayed sleep phase.

(DOCX)

Click here for additional data file. (16.3KB, docx)
S12 Table. P-values from burden testing of rare (MAF < 0.01%) loss-of-function and missense variants in genes outlined in this paper on self-reported sleep duration in UK Biobank.

(DOCX)

Click here for additional data file. (18.3KB, docx)
S13 Table. P-values from burden testing of rare (MAF < 0.01%) loss-of-function and missense variants in genes outlined in this paper on chronotype in UK Biobank.

(DOCX)

Click here for additional data file. (17.5KB, docx)
S14 Table. P-values from burden testing of rare (MAF < 0.01%) loss-of-function and missense variants in genes previously reported to harbour variants causal for disruptive sleep duration or timing on accelerometer estimates of sleep duration in UK Biobank.

There were no remaining loss-of-function carriers for GRM1, ADRB1 and CRY2 within the subset of individuals from UK Biobank who wore an accelerometer.

(DOCX)

Click here for additional data file. (18.3KB, docx)
S15 Table. P-values from burden testing of rare (MAF < 0.01%) loss-of-function and missense variants in genes previously reported to harbour variants causal for disruptive sleep duration or timing on accelerometer estimates of sleep timing in UK Biobank.

There were no remaining loss-of-function carriers for GRM1, ADRB1 and CRY2 within the subset of individuals from UK Biobank who wore an accelerometer.

(DOCX)

Click here for additional data file. (17.2KB, docx)
S16 Table. Summary of chronotype by PER2 loss-of-function carrier status in the UK Biobank.

(DOCX)

Click here for additional data file. (14.3KB, docx)
S17 Table. Summary of sleep-midpoint by PER2 loss-of-function carrier status in the UK Biobank.

(DOCX)

Click here for additional data file. (14.2KB, docx)
S18 Table. Summary of L5-midpoint timing by PER2 loss-of-function carrier status in the UK Biobank.

(DOCX)

Click here for additional data file. (14.6KB, docx)
S1 Methods. Supplementary Methods.

Description of Additional Studies.

(DOCX)

Click here for additional data file. (49.6KB, docx)

Acknowledgments

We thank Louis Ptáček, Alina Patke and Michael Young for helpful discussion and comments on the manuscript. We gratefully acknowledge the studies and participants who provided biological samples and data for MESA and TOPMed. The Finnish data used for the research was obtained from THL Biobank (study number BB2019_43). We would like to thank all study participants for their generous participation at THL Biobank and cohorts FINRISK 1992, 1997, 2002, 2007, and 2012, as well as Health 2000 and 2011. We want to acknowledge the participants and investigators of the FinnGen study.

Data Availability

Data cannot be shared publicly because of data availability and data return policies of the UK Biobank. Data are available from the UK Biobank for researchers who meet the criteria for access to datasets to UK Biobank (http://www.ukbiobank.ac.uk).

Funding Statement

M.N.W. was supported by grant MR/M005070/1 from Medical Research Council. A.R.W is supported by the Academy of Medical Sciences / the Wellcome Trust / the Government Department of Business, Energy and Industrial Strategy / the British Heart Foundation / Diabetes UK Springboard Award [SBF006\1134]. R.N.B is supported by grant MR/T00200X/1 from Medical Research Council. Whole-genome sequencing for “NHLBI TOPMed: Multi-Ethnic Study of Atherosclerosis (MESA)” was supported by grant phs001416.v1.p1 from the National Heart, Lung, and Blood Institute. The MESA study was supported in part by the National Center for Advancing Translational Sciences, CTSI grant UL1TR001881, and the National Institute of Diabetes and Digestive and Kidney Disease Diabetes Research Center (DRC) grant DK063491 to the Southern California Diabetes Endocrinology Research Center. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

References

  • 1.Shi G, Xing L, Wu D, Bhattacharyya BJ, Jones CR, McMahon T, et al. A Rare Mutation of beta1-Adrenergic Receptor Affects Sleep/Wake Behaviors. Neuron. 2019;103(6):1044–55 e7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.Shi G, Yin C, Fan Z, Xing L, Mostovoy Y, Kwok PY, et al. Mutations in Metabotropic Glutamate Receptor 1 Contribute to Natural Short Sleep Trait. Curr Biol. 2020. doi: 10.1016/j.cub.2020.09.071 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Xing L, Shi G, Mostovoy Y, Gentry NW, Fan Z, McMahon TB, et al. Mutant neuropeptide S receptor reduces sleep duration with preserved memory consolidation. Sci Transl Med. 2019;11(514). doi: 10.1126/scitranslmed.aax2014 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.He Y, Jones CR, Fujiki N, Xu Y, Guo B, Holder JL Jr., et al. The transcriptional repressor DEC2 regulates sleep length in mammals. Science. 2009;325(5942):866–70. doi: 10.1126/science.1174443 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Toh KL, Jones CR, He Y, Eide EJ, Hinz WA, Virshup DM, et al. An hPer2 phosphorylation site mutation in familial advanced sleep phase syndrome. Science. 2001;291(5506):1040–3. doi: 10.1126/science.1057499 [DOI] [PubMed] [Google Scholar]
  • 6.Zhang L, Hirano A, Hsu PK, Jones CR, Sakai N, Okuro M, et al. A PERIOD3 variant causes a circadian phenotype and is associated with a seasonal mood trait. Proc Natl Acad Sci U S A. 2016;113(11):E1536–44. doi: 10.1073/pnas.1600039113 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Hirano A, Shi G, Jones CR, Lipzen A, Pennacchio LA, Xu Y, et al. A Cryptochrome 2 mutation yields advanced sleep phase in humans. Elife. 2016;5. doi: 10.7554/eLife.16695 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Xu Y, Padiath QS, Shapiro RE, Jones CR, Wu SC, Saigoh N, et al. Functional consequences of a CKIdelta mutation causing familial advanced sleep phase syndrome. Nature. 2005;434(7033):640–4. doi: 10.1038/nature03453 [DOI] [PubMed] [Google Scholar]
  • 9.Kurien P, Hsu PK, Leon J, Wu D, McMahon T, Shi G, et al. TIMELESS mutation alters phase responsiveness and causes advanced sleep phase. Proc Natl Acad Sci U S A. 2019;116(24):12045–53. doi: 10.1073/pnas.1819110116 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Patke A, Murphy PJ, Onat OE, Krieger AC, Ozcelik T, Campbell SS, et al. Mutation of the Human Circadian Clock Gene CRY1 in Familial Delayed Sleep Phase Disorder. Cell. 2017;169(2):203–15 e13. doi: 10.1016/j.cell.2017.03.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Sudlow C, Gallacher J, Allen N, Beral V, Burton P, Danesh J, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12(3):e1001779. doi: 10.1371/journal.pmed.1001779 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Tuke MA, Ruth KS, Wood AR, Beaumont RN, Tyrrell J, Jones SE, et al. Mosaic Turner syndrome shows reduced penetrance in an adult population study. Genet Med. 2019;21(4):877–86. doi: 10.1038/s41436-018-0271-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Wright CF, West B, Tuke M, Jones SE, Patel K, Laver TW, et al. Assessing the Pathogenicity, Penetrance, and Expressivity of Putative Disease-Causing Variants in a Population Setting. Am J Hum Genet. 2019;104(2):275–86. doi: 10.1016/j.ajhg.2018.12.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Jones SE, Lane JM, Wood AR, van Hees VT, Tyrrell J, Beaumont RN, et al. Genome-wide association analyses of chronotype in 697,828 individuals provides insights into circadian rhythms. Nat Commun. 2019;10(1):343. doi: 10.1038/s41467-018-08259-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.Weedon MN, Jackson L, Harrison JW, Ruth KS, Tyrrell J, Hattersley AT, et al. Use of SNP chips to detect rare pathogenic variants: retrospective, population based diagnostic evaluation. BMJ. 2021;372:n214. doi: 10.1136/bmj.n214 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Weedon MN, Jackson L, Harrison JW, Ruth KS, Tyrrell J, Hattersley AT, et al. Assessing the analytical validity of SNP-chips for detecting very rare pathogenic variants: implications for direct-to-consumer genetic testing. bioRxiv, doi: 101101/696799 2019. [Google Scholar]
  • 17.Szustakowski JD, Balasubramanian S, Kvikstad E, Khalid S, Bronson PG, Sasson A, et al. Advancing human genetics research and drug discovery through exome sequencing of the UK Biobank. Nat Genet. 2021;53(7):942–8. doi: 10.1038/s41588-021-00885-0 [DOI] [PubMed] [Google Scholar]
  • 18.Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, et al. Integrative genomics viewer. Nat Biotechnol. 2011;29(1):24–6. doi: 10.1038/nbt.1754 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 19.Lane JM, Jones SE, Dashti HS, Wood AR, Aragam KG, van Hees VT, et al. Biological and clinical insights from genetics of insomnia symptoms. Nat Genet. 2019;51(3):387–93. doi: 10.1038/s41588-019-0361-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Ashbrook LH, Krystal AD, Fu YH, Ptacek LJ. Genetics of the human circadian clock and sleep homeostat. Neuropsychopharmacology. 2020;45(1):45–54. doi: 10.1038/s41386-019-0476-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Jones SE, van Hees VT, Mazzotti DR, Marques-Vidal P, Sabia S, van der Spek A, et al. Genetic studies of accelerometer-based sleep measures yield new insights into human sleep behaviour. Nat Commun. 2019;10(1):1585. doi: 10.1038/s41467-019-09576-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Borodulin K, Tolonen H, Jousilahti P, Jula A, Juolevi A, Koskinen S, et al. Cohort Profile: The National FINRISK Study. Int J Epidemiol. 2018;47(3):696–i. doi: 10.1093/ije/dyx239 [DOI] [PubMed] [Google Scholar]
  • 23.Bild DE, Bluemke DA, Burke GL, Detrano R, Diez Roux AV, Folsom AR, et al. Multi-Ethnic Study of Atherosclerosis: objectives and design. Am J Epidemiol. 2002;156(9):871–81. doi: 10.1093/aje/kwf113 [DOI] [PubMed] [Google Scholar]
  • 24.McLaren W, Gil L, Hunt SE, Riat HS, Ritchie GR, Thormann A, et al. The Ensembl Variant Effect Predictor. Genome Biol. 2016;17(1):122. doi: 10.1186/s13059-016-0974-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Karczewski KJ, Francioli LC, Tiao G, Cummings BB, Alfoldi J, Wang Q, et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature. 2020;581(7809):434–43. doi: 10.1038/s41586-020-2308-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Mbatchou J, Barnard L, Backman J, Marcketta A, Kosmicki JA, Ziyatdinov A, et al. Computationally efficient whole genome regression for quantitative and binary traits. BioRxiv. 2021. doi: 10.1038/s41588-021-00870-7 [DOI] [PubMed] [Google Scholar]
  • 27.Bae K, Jin X, Maywood ES, Hastings MH, Reppert SM, Weaver DR. Differential functions of mPer1, mPer2, and mPer3 in the SCN circadian clock. Neuron. 2001;30(2):525–36. doi: 10.1016/s0896-6273(01)00302-6 [DOI] [PubMed] [Google Scholar]
  • 28.Xu Y, Toh KL, Jones CR, Shin JY, Fu YH, Ptacek LJ. Modeling of a human circadian mutation yields insights into clock regulation by PER2. Cell. 2007;128(1):59–70. doi: 10.1016/j.cell.2006.11.043 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Khera AV, Chaffin M, Aragam KG, Haas ME, Roselli C, Choi SH, et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat Genet. 2018;50(9):1219–24. doi: 10.1038/s41588-018-0183-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Smieszek SP, Brzezynski JL, Kaden AR, Shinn JA, Wang J., Xiao C., et al. An Observational Study Investigating the CRY1Δ11 Variant Associated with Delayed Sleep-wake Patterns and Circadian Metabolic Output. PREPRINT Research Square, doi: 10.1038/s41598-021-99418-2 2021. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Munafo MR, Tilling K, Taylor AE, Evans DM, Davey Smith G. Collider scope: when selection bias can substantially influence observed associations. Int J Epidemiol. 2018;47(1):226–35. doi: 10.1093/ije/dyx206 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.van Hees VT, Sabia S, Jones SE, Wood AR, Anderson KN, Kivimaki M, et al. Estimating sleep parameters using an accelerometer without sleep diary. Sci Rep. 2018;8(1):12975. doi: 10.1038/s41598-018-31266-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Dashti HS, Jones SE, Wood AR, Lane JM, van Hees VT, Wang H, et al. Genome-wide association study identifies genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates. Nat Commun. 2019;10(1):1100. doi: 10.1038/s41467-019-08917-4 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Zheng B, Larkin DW, Albrecht U, Sun ZS, Sage M, Eichele G, et al. The mPer2 gene encodes a functional component of the mammalian circadian clock. Nature. 1999;400(6740):169–73. doi: 10.1038/22118 [DOI] [PubMed] [Google Scholar]
  • 35.Takahashi JS, Hong HK, Ko CH, McDearmon EL. The genetics of mammalian circadian order and disorder: implications for physiology and disease. Nat Rev Genet. 2008;9(10):764–75. doi: 10.1038/nrg2430 [DOI] [PMC free article] [PubMed] [Google Scholar]

Decision Letter 0

Gregory S Barsh

3 May 2022

Dear Dr Wood,

Thank you very much for submitting your Research Article entitled 'The impact of Mendelian sleep and circadian genetic variants in a population setting' to PLOS Genetics.

The manuscript was fully evaluated at the editorial level and by two independent peer reviewers. As you will see, both reviewers are positive, but there are some concerns that we ask you address in a revised manuscript

We therefore ask you to modify the manuscript according to the review recommendations. Your revisions should address the specific points made by each reviewer.

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Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: I very much enjoyed reading the authors’ manuscript. It was nice study into the effects of previously found genetic variants linked to sleep and circadian genes. Whilst slightly limited in scope, it is nevertheless an important piece of research.

There were a few things in particular that I very much enjoyed in the paper. Mostly I found the paper easy to read and understand with only a few confusing sections (see below). Verification of the results from the main cohort (UK Biobank) in the two Finnish studies was nice to see and really helps to back up the mostly null results presented in the manuscript. Lastly the detailed sensitivity analysis was very useful in understanding the effects and showing that results presented were not caused by cherry picking of thresholds etc.

That being said I did have a few criticisms of the manuscript, these are mostly minor comments/changes.

• Sleep timing – I feel that “diurnal preference” and “chronotype” are used interchangeably here, with only the former being defined. I would either stick to one, or define both.

• Line 256: redundant word “tests”

• Confusing sub headings. In the Methods section subheadings are in mix of italics and underlined but no consistently. Later in the Results subheadings are just in italics. I felt that the lack of consistency was confusing.

• I don’t think that the logistic regression analyses where presented in full anywhere. As far as I could tell they were given in select cases, the full results from this for all analyses run should be presented somewhere (probably in supplemental).

• Tables 1 and 2 didn’t seem very interesting to me. For the first section in the results as far as I understood the most important analysis was in Sup Table 1. I’d recommend moving T1&2 to supplemental and promoting ST1 to the manuscript.

• ST2: Is the 2-sided t-test correct here? If you’re looking for evidence that individuals have a short sleep, should it not be a 1-sided test? I think similar arguments can be made for later tables as well.

• Line 347: “We previously tested the …” Is this missing a reference here? Likewise on Line 363 “has previously been associated with…” also seems to missing a reference. Either needs references adding in or these sentences need removing.

• Table 5 seems to show an association between sleep midpoint and the CRY2 variant in the accelerometer data, but this isn’t mentioned in the text?

• ST5: Is the “definitely a morning person” section of this table the same as the “definitely a morning person” section of Table 5? If so then why are the Finnish values different in these two tables? Also the p<0.0001 should match the p<0.001 in Table 5 for consistency.

• I would switch the order of ST6 and ST7 (and likewise the order of ST9 and ST10) to match the ordering in Table 5 (and Table 6).

• I think that it’s worth mentioning the observed differences between weekday and weekend nights in ST9

• Supplemental Figures 1-3 add nothing to the manuscript as they are not referred to (just tacked on to line 374). They should be removed.

Reviewer #2: This is a straightforward publication that helps clarify the literature. This is an important and well-done publication. Non-replications, including from the same author’s lab where the work has been initially generated, are important as it is harder and harder to make sense of the literature.

The authors show lack of replication of large effects of 8/10 previously published “mendelian circadian and sleep variants” reported in single or rare families. The authors also extend the study of these genes by conducting burden tests, often finding minimal or no effects, except for PER2 and PER3. It is then argued that out of all 10 mutations previously reported maybe only one, S662G, not found in this sample, maybe pathogenic. The other previously published mutation not found in this survey was NPSR1 Y206H.

I have a few comments. It would be useful to list in the abstract the two out of 10 mutations that were not found (NPSR1 Y206H and PER2 S662G).

The publication focusses mostly on circadian and short sleep variants, so the title is misleading. For example, a new mutation g.42184347T>C; p.Lys68Arg; rs537376938 in the cleavage site of HCRT has been recently reported to be associated with idiopathic hypersomnia (minor allele frequency of 1.67% in cases versus 0.32% in controls, P = 2.7 × 10-8, odds ratio = 5.36) characterized by sleepiness and excessive sleep in Asians (Miyagawa et al. NPJ Genom Med. 2022 Apr 12;7(1):29). This was somewhat surprising as a mouse model with a truncated form of pre-proorexin generating only orexin A, even when homozygous, has no phenotype. Miyagawa et al. reports in the publication that the variant is present in non-Finnish Europeans at 0.013%, so most likely it would be present in the UK biobank analyzed here. Perhaps analyzing this variant should be confirmed and included, as the UK biobank has questions on excessive sleepiness and sleep time. In this case, the title would be more accurate.

The main issues that could have led to a difference in results include: 1) ascertainment of phenotypes, 2) amount of cases/nature of pedigree (the more polygenic a trait is the faster genetic risk decreases from one generation to the next) and 3) genetic background/ethnicity (even so MESA was included, it is a small sample). These limitations are not all well discussed. Even in the small number of cases identified with some of these mutations, a PCA analysis could reveal unsuspected population stratification or founder effects. Has this been looked at?

The fact many of these variants had thorough functional characterization (often in mouse) that was used as evidence of involvement is not discussed at all. The authors need to discuss briefly the limits of functional characterization. Indeed, it is argued that PER2 S662G has sufficient evidence for being pathogenic. Indeed, Weeldon et al show here that haploinsufficiency of PER2 is associated with a morning phenotype. PER2 knock out are also phase advanced (Nature; 1999 Jul 8;400(6740):169-73). Finally, for S662G, there is multiple in vitro evidence linking the S662 phosphorylation site with casein kinases (although the exact mechanism is somewhat debated see Cell. 2007 Jan 12;128(1):22-3. doi: 10.1016/j.cell.2006.12.024), other key circadian genes, plus the effects of S662G and S662A were assessed in mice. This is reasonable.

However, there is also similar rodent evidence be written for other mutations that was used to “confirm” the phenotype that was not confirmed in this study such 1) the T44A in the human CKIδ gene had a lot of experiment support; 2) the A260T of CRY2 which is in its FAD binding domain (postulated to increase the affinity of FAD for the E3 ubiquitin ligase FBXL3, thus promoting its degradation). 3) the Patke et al. CRY1 DSPD mutation, located in the 5′ splice site of exon 11 and leading to exon 11 and an in-frame deletion of 24 residues in the C-terminal region of CRY1. This was postulated to lead to an enhanced affinity of this repressor for the circadian activator proteins CLOCK and BMAL1, which could lengthen the period of circadian molecular rhythms. These also all had reasonable explanations for the phenotype observed. This raises questions on the value of functional characterizations done in mouse or other models that should be briefly mentioned.

Similarly, NPSR1 Y206H which is not found is not discussed. Is there something special about the NPSR1 variant or this gene?

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Genetics data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes: Robert Maidstone

Reviewer #2: No

Decision Letter 1

Gregory S Barsh

26 Jul 2022

Dear Dr Wood,

We are pleased to inform you that your manuscript entitled "The impact of Mendelian sleep and circadian genetic variants in a population setting" has been editorially accepted for publication in PLOS Genetics. Congratulations!

The revised manuscript was seen by the previous reviewers, both of whom are enthusiastic as you will see below.

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Editor-in-Chief

PLOS Genetics

Gregory Copenhaver

Editor-in-Chief

PLOS Genetics

www.plosgenetics.org

Twitter: @PLOSGenetics

----------------------------------------------------

Comments from the reviewers (if applicable):

Reviewer's Responses to Questions

Comments to the Authors:

Please note here if the review is uploaded as an attachment.

Reviewer #1: I thank the authors for their response to my previous comments. I feel that they have sufficiently answered all of my points and I'm happy with the resulting manuscript. I look forward to seeing it published.

Reviewer #2: The revised version addresses all my comments.

**********

Have all data underlying the figures and results presented in the manuscript been provided?

Large-scale datasets should be made available via a public repository as described in the PLOS Genetics data availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.

Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: Yes: Robert Maidstone

Reviewer #2: No

----------------------------------------------------

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Acceptance letter

Gregory S Barsh

23 Aug 2022

PGENETICS-D-22-00319R1

The impact of Mendelian sleep and circadian genetic variants in a population setting

Dear Dr Wood,

We are pleased to inform you that your manuscript entitled "The impact of Mendelian sleep and circadian genetic variants in a population setting" has been formally accepted for publication in PLOS Genetics! Your manuscript is now with our production department and you will be notified of the publication date in due course.

The corresponding author will soon be receiving a typeset proof for review, to ensure errors have not been introduced during production. Please review the PDF proof of your manuscript carefully, as this is the last chance to correct any errors. Please note that major changes, or those which affect the scientific understanding of the work, will likely cause delays to the publication date of your manuscript.

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Thank you again for supporting PLOS Genetics and open-access publishing. We are looking forward to publishing your work!

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PLOS Genetics

On behalf of:

The PLOS Genetics Team

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Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 Table. Summary of twelve variants previously reported to be causal for Mendelian sleep and circadian conditions, including the variant frequencies catalogued in gnomAD.

    (DOCX)

    Click here for additional data file. (15.7KB, docx)
    S2 Table. Maximum genotype counts for 12 previously reported monogenic causes of sleep and circadian conditions in unrelated individuals of European ancestry from the UK Biobank, FINRISK / Health 2000–2011, and MESA studies.

    Genotype counts are based on availability of sleep characteristics relevant to each gene.

    (DOCX)

    Click here for additional data file. (17.1KB, docx)
    S3 Table. Summary statistics of self-reported sleep duration in the UK Biobank (Field 1160) for carriers of variants previously described as causal for familial natural short sleep.

    (DOCX)

    Click here for additional data file. (19.9KB, docx)
    S4 Table. Summary statistics of dichotomised self-reported sleep data in the UK Biobank for carriers of variants previously described as causal for familial natural short sleep.

    Data unavailable for self-reported sleep of ≤5 hours, ≤4 hours and 4–6 hours in the Finnish study.

    (DOCX)

    Click here for additional data file. (20.9KB, docx)
    S5 Table. Summary statistics of accelerometer-derived estimates of sleep duration in UK Biobank for carriers of variants previously described as casual for familial natural short sleep.

    No carriers of the GRM1 A889T variant remained among individuals from UK Biobank who had worn an accelerometer.

    (DOCX)

    Click here for additional data file. (17.8KB, docx)
    S6 Table. Summary statistics of “Morningness” across genotype groups for variants previously reported as causal for familial advanced sleep phase.

    Data on being “more or definitely a morning person” unavailable in the Finnish studies.

    (DOCX)

    Click here for additional data file. (18.5KB, docx)
    S7 Table. Summary statistics of L5-midpoint timing estimated from accelerometer data in UK Biobank across genotype groups for variants previously reported as causal for familial advanced sleep phase.

    (DOCX)

    Click here for additional data file. (18.2KB, docx)
    S8 Table. Summary statistics of sleep-midpoint estimated from accelerometer data in UK Biobank and MESA across genotype groups for variants previously reported as causal for familial advanced sleep phase.

    (DOCX)

    Click here for additional data file. (20.4KB, docx)
    S9 Table. Summary statistics of “eveningness” across genotype groups for variants previously reported as causal for delayed sleep phase.

    Data on being “more or definitely an evening person” unavailable in the Finnish studies.

    (DOCX)

    Click here for additional data file. (15.4KB, docx)
    S10 Table. Summary statistics of L5-midpoint timing estimated from accelerometer data in UK Biobank a across genotype groups for variants previously reported as causal for delayed sleep phase.

    (DOCX)

    Click here for additional data file. (15.2KB, docx)
    S11 Table. Summary statistics of sleep-midpoint estimated from accelerometer data in UK Biobank and MESA across genotype groups for variants previously reported as causal for delayed sleep phase.

    (DOCX)

    Click here for additional data file. (16.3KB, docx)
    S12 Table. P-values from burden testing of rare (MAF < 0.01%) loss-of-function and missense variants in genes outlined in this paper on self-reported sleep duration in UK Biobank.

    (DOCX)

    Click here for additional data file. (18.3KB, docx)
    S13 Table. P-values from burden testing of rare (MAF < 0.01%) loss-of-function and missense variants in genes outlined in this paper on chronotype in UK Biobank.

    (DOCX)

    Click here for additional data file. (17.5KB, docx)
    S14 Table. P-values from burden testing of rare (MAF < 0.01%) loss-of-function and missense variants in genes previously reported to harbour variants causal for disruptive sleep duration or timing on accelerometer estimates of sleep duration in UK Biobank.

    There were no remaining loss-of-function carriers for GRM1, ADRB1 and CRY2 within the subset of individuals from UK Biobank who wore an accelerometer.

    (DOCX)

    Click here for additional data file. (18.3KB, docx)
    S15 Table. P-values from burden testing of rare (MAF < 0.01%) loss-of-function and missense variants in genes previously reported to harbour variants causal for disruptive sleep duration or timing on accelerometer estimates of sleep timing in UK Biobank.

    There were no remaining loss-of-function carriers for GRM1, ADRB1 and CRY2 within the subset of individuals from UK Biobank who wore an accelerometer.

    (DOCX)

    Click here for additional data file. (17.2KB, docx)
    S16 Table. Summary of chronotype by PER2 loss-of-function carrier status in the UK Biobank.

    (DOCX)

    Click here for additional data file. (14.3KB, docx)
    S17 Table. Summary of sleep-midpoint by PER2 loss-of-function carrier status in the UK Biobank.

    (DOCX)

    Click here for additional data file. (14.2KB, docx)
    S18 Table. Summary of L5-midpoint timing by PER2 loss-of-function carrier status in the UK Biobank.

    (DOCX)

    Click here for additional data file. (14.6KB, docx)
    S1 Methods. Supplementary Methods.

    Description of Additional Studies.

    (DOCX)

    Click here for additional data file. (49.6KB, docx)
    Attachment

    Submitted filename: 20220706_PlosGenetics_Response_to_Reviewers.docx

    Click here for additional data file. (88.7KB, docx)

    Data Availability Statement

    Data cannot be shared publicly because of data availability and data return policies of the UK Biobank. Data are available from the UK Biobank for researchers who meet the criteria for access to datasets to UK Biobank (http://www.ukbiobank.ac.uk).

    Articles from PLoS Genetics are provided here courtesy of PLOS

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