J Sleep Res. 2019;00:e12925. wileyonlinelibrary.com/journal/jsr  |  1 of 9 https://doi.org/10.1111/jsr.12925 © 2019 European Sleep Research Society Received: 9 April 2019  |  Revised: 20 August 2019  |  Accepted: 4 September 2019 DOI: 10.1111/jsr.12925 R E G U L A R R E S E A R C H P A P E R Variation near MTNR1A associates with early development and interacts with seasons Sonja Sulkava1,2  | Antti‐Mathias Taka1,2 | Katri Kantojärvi1,2  | Pirjo Pölkki3 | Isabel Morales‐Muñoz1,4 | Lili Milani5  | Tarja Porkka‐Heiskanen6  | Outi Saarenpää‐ Heikkilä7  | Anneli Kylliäinen8  | E. Juulia Paavonen1,9  | Tiina Paunio1,2 Sulkava and Taka should be considered joint first authors. 1Department of Public Health Solutions, Finnish Institute for Health and Welfare, Helsinki, Finland 2Department of Psychiatry and SleepWell Research Program, Faculty of Medicine, University of Helsinki and Helsinki University Central Hospital, Helsinki, Finland 3Department of Social Sciences, University of Eastern Finland, Kuopio, Finland 4Institute for Mental Health, School of Psychology, University of Birmingham, Birmingham, UK 5Estonian Genome Center, Institute of Genomics, University of Tartu, Tartu, Estonia 6Institute of Biomedicine/ Physiology, University of Helsinki, Helsinki, Finland 7Tampere Center for Child Health Research, Tampere University and Tampere University Hospital, Tampere, Finland 8Psychology, Faculty of Social Sciences, Tampere University, Tampere, Finland 9Pediatric Research Center, Child Psychiatry, University of Helsinki and Helsinki University Hospital, Helsinki, Finland Correspondence Sonja Sulkava and Tiina Paunio, Department of Public Health Solutions, Finnish Institute for Health and Welfare, Haartmaninkatu 8, 00290 Helsinki, Biomedicum 1, Helsinki, Finland. Email: sonja.sulkava@thl.fi; tiina.paunio@ thl.fi Funding information Emil Aaltosen Säätiö; Suomen Akatemia, Grant/Award Number: 134880, 253346 Summary Melatonin is a circadian regulatory hormone with neuroprotective properties. We have previously demonstrated the association of the genetic variant rs12506228 near the melatonin receptor 1A gene (MTNR1A) with intolerance to shift‐work. Furthermore, this variant has been connected to Alzheimer's disease. Because of the previously suggested role of melatonin signalling in foetal neurocognitive and sleep develop‐ ment, we studied here the association of rs12506228 with early development. The study sample comprised 8‐month‐old infants from the Finnish CHILD‐SLEEP birth cohort (n = 1,301). Parental questionnaires assessed socioemotional, communica‐ tion and motor development, as well as sleep length and night awakenings. The A allele of rs12506228 showed an association with slower socioemotional (p = .025) and communication (p = .0098) development, but no direct association with sleep. However, the association of the Finnish seasons with infant sleep length interacted with rs12506228. Taken together, rs12506228 near MTNR1A, which has been previ‐ ously linked to adult and elderly traits, is shown here to associate with slower early cognitive development. In addition, these results suggest that the darker seasons as‐ sociate with longer infant sleep time, but only in the absence of the rs12506228 AA genotype. Because the risk allele has been connected to fewer brain MT1 melatonin receptors, these associations may reflect the influence of decreased melatonin signal‐ ling in early development. K E Y W O R D S cognitive development, genetics, infants, MTNR1A, seasonal variation, sleep 2 of 9  |     SULKAVA et AL. and 308588; Signe ja Ane Gyllenbergin Säätiö; Lastentautien Tutkimussäätiö; Maud Kuistilan Muistosäätiö 1  | INTRODUC TION A newborn sleeps about 15 hr a day, and acquires a diurnal sleep– wake rhythm usually within the first 6 months of age (Mindell et al., 2016). The rhythmic secretion of melatonin from the pineal gland also develops during this period. (Kennaway, Goble, & Stamp, 1996; Kennaway, Stamp, & Goble, 1992). Prenatally, melatonin is acquired through the placenta from the mother, who produces it in high levels at the end of the pregnancy (Nakamura et al., 2001). Adequate sleep is essential for normal brain development and synaptic plasticity (Tononi & Cirelli, 2014). Previous studies have demonstrated that sleep is associated with enhanced development of memory, learning and language (Tham, Schneider, & Broekman, 2017). Sleep traits have also been used to predict the cognitive, psy‐ chomotor and temperament development of children (Ednick et al., 2009). According to a twin‐study of 18‐month‐old infants, genetic factors have a moderate effect on sleep early in life (Brescianini et al., 2011). However, only a few studies have addressed the effects of genes on sleep in infancy. The foundations of developmental domains are built during the first year of life: children begin to engage in social interactions, cooing changes to babbling, they become able to adapt posturally to different situations, and emotions start to take shape (Zeanah, Boris, & Larrieu, 1997). Synaptogenesis is at its highest in the first year of life, peaking in the Angular gyrus and Broca's area at the age of 8 months, coinciding with the substantial development of higher cognition (Thompson & Nelson, 2001). Similarly to socioemotional development, a child's vocalisation as part of their communication abilities develops dramatically during the first year (Levine, Strother‐ Garcia, Golinkoff, & Hirsh‐Pasek, 2016). Both the environment and genetics affect a child's socioemotional (DiLalla, Mullineaux, & Biebl, 2012) and communication development (Hayiou‐Thomas, 2008). In the present study, we focussed on the associations of rs12506228 with child development and sleep in the first 8 months of life. rs12506228 is located in the q arm of chromosome 4, 72 kb downstream of MTNR1A, which is the closest confirmed protein cod‐ ing gene. This variant was previously identified as a top finding in a genome‐wide study of job‐related exhaustion in shift‐workers (as an estimate of sensitivity to circadian disruption; Sulkava et al., 2017). A strong association with old age Alzheimer's disease has also been re‐ ported for the A allele of the variant (Sulkava et al., 2018). In previous studies, risk genes associated with Alzheimer's disease and multiple mental illnesses have been suggested to affect the brain as early as infancy (Knickmeyer et al., 2014). It has also been suggested that the A allele of rs12506228 decreases the expression of MTNR1A by methylation of the gene (Sulkava et al., 2017). Lower melatonin sig‐ nalling has been linked to problems in foetal rhythmicity and normal neurocognitive development during the foetal period (Reiter, Tan, Korkmaz, & Rosales‐Corral, 2014). We hypothesised that the MTNR1A variant would have a neg‐ ative effect on sleep quality because of the association with lower brain expression of MT1 melatonin receptors. If expression of these receptors is reduced, the circadian cycle would consequently be weaker (Sulkava et al., 2017), which could lead to disrupted sleep. Because of the suggested importance of melatonin signalling in brain development during the foetal period, we further hypothesised that the MTNR1A risk variant would also be associated with early devel‐ opment in infants. In this study, we investigated the association of rs12506228 with sleep traits (total sleep time, diurnal/total sleep time ratio and night awakenings), and with socioemotional, com‐ munication and motor development in 8‐month‐old infants using questionnaire‐based data as well as actigraphy‐based data for sleep length. As seasonal light conditions regulate the amount of mela‐ tonin secreted (Luboshitzky et al., 1998), we also studied the effects of seasons on infant sleep and interactions with the MTNR1A genetic variant. 2  | MATERIAL S AND METHODS 2.1 | Participants This study is part of the CHILD‐SLEEP cohort study of infants born between April 2011 and February 2013 in the Pirkanmaa area in Finland. The Ethical Committee of Pirkanmaa Hospital District ap‐ proved the study protocol (R11032/9.3.2011), and the principles of the Declaration of Helsinki were followed. The parents gave their written informed consent. In our study, we examined the de‐ velopment and sleep of these infants at the age of 8 months. The initial number of participating families was 1,673, and at the age of 8 months the response rate was 77.8% (N = 1,301). Prenatal charac‐ teristics for the study sample and those who dropped out are shown in Table S1. No differences in the allele frequency of rs12506228 were detected between the groups. Families that dropped out were less likely to have parental university education, and were more likely to have maternal depression and at least one older sibling. However, these characteristics did not associate with our explanatory vari‐ ables (rs12506228 and season of examination), thus they are not confounding factors in this study. Of the families participating in the 8‐month data point, 87.5% (N = 1,139) had provided umbilical cord blood samples for DNA extraction and genotyping. Six participants, whose ages were over 10 months, were removed from the study. Sample sizes varied slightly depending on the variable, ranging from 1,034 (night awakenings) to 1,124 (socioemotional development). We also used actigraphy to objectively analyse the nocturnal sleep time of 354 infants, 314 of which were aged between 7 months and      |  3 of 9SULKAVA et AL. 10 months and had genetic data available for rs12506228 (Martin & Hakim, 2011). Most of the infants (n = 1,290, 99.5%) were at home with their parent, and only six infants (0.2%) were in daycare full‐ time or part‐time. 2.2 | Collection of data 2.2.1 | Measures The domains of development and sleep were measured by deliver‐ ing specific questionnaires to the parents. The questions regarding sleep were multifaceted and designed to map out disturbances in different aspects of infant sleep behaviour (Sadeh, 2004). We were primarily interested in sleeping time including total sleep time as well as differences in nocturnal and diurnal sleep. Nocturnal sleep time was measured between 19:00 hours and 07:00 hours, and di‐ urnal sleep between 07:00 hours and 19:00 hours. Total sleep time was calculated as the sum of diurnal and nocturnal sleep time. Children with total sleep time > 17 hr were removed as outliers (n = 3). In addition to time spent sleeping, we assessed the number of night awakenings according to the Infant Sleep Questionnaire (Morrell, 1999) by asking, “How many times does your baby wake each night and need resettling on average” between 00:00 hours and 06:00 hours. Response choices ranged from 0 to 5 or more times. In addition to these subjective questionnaires, we were able to objectively measure sleep with actigraphy; 353 participants aged from 7 months to 10 months wore an actigraphy bracelet on their thigh for 3 days and nights. This was used for second‐ ary analysis to confirm the questionnaire's results. Finnish 8‐ month‐old infants sleep their daytime naps not only in bed but often also in a moving pram, which makes the actigraphy meas‐ urement of day sleep less reliable compared with nocturnal sleep. Therefore, only the actigraphy measurement of the nocturnal ac‐ tual sleep time was considered in this study. Figures for individual actograms were made with MotionWare Software (version 1.1.26, CamNTech: https ://www.camnt ech.com/produ cts/motio nwatc h/ motio nware‐software). Socioemotional, communication and gross motor develop‐ ment were assessed according to a specific Finnish parent‐rated questionnaire modified for the study (Lyytinen, Ahonen, Eklund, F I G U R E 1  Six examples of actograms for different infants in summer, winter and autumn/spring seasons, among carriers of the CC genotype and AA genotype of rs12506228. The period highlighted in grey indicates the nocturnal sleep period that was used for the analysis. The carrier of CC genotype of rs12506228 studied in summer (a) shows a shorter night‐time sleep duration (mean nocturnal sleep length = 5 hr 52 min) than the infant studied in winter (b; mean nocturnal sleep length = 10 hr 32 min), or in autumn/spring (c; mean nocturnal sleep length = 8 hr 23 min). Carriers of the AA genotype at 8 months of age have a more similar night‐time sleep duration, independent of the season of actigraphy assessment (d; mean nocturnal sleep length = 8 hr 48 min; e; mean nocturnal sleep length = 8 hr 32 min; f; mean nocturnal sleep length 8 hr 56 min) 4 of 9  |     SULKAVA et AL. & Lyytinen, 2000; Nieminen & Korpela, 2004). Our main interest was cognitive development, but gross motor development was included as a non‐cognitive domain of development to study the specificity of the associations with cognitive development. The questionnaire included 13, 10 and 12 statements of socioemo‐ tional, communication and gross motor development, respec‐ tively. The statements were designed to be easy to observe by the parents, for example “Shows fear of strangers by getting serious or crying when a new adult approaches” in the socioemotional scale, or “Can get to sitting position without help” in the gross motor scale. The parents were asked to rate the statements about their infant's development according to three choices: never detected (0 points); detected once (1 point); and detected multiple times (2 points). The answers were rated from 0 to 2, and were summed together within each scale. 2.2.2 | Measures for seasonal analyses The study subjects were living in the Pirkanmaa region of Finland, sit‐ uated in latitude 61°N. We divided the participants into three groups according to the season when they answered the questionnaire: win‐ ter (November to January), summer (May to July) and autumn/spring (August to October and February to April). Summer was coded as 1, spring and fall 2, and winter 3 according to increasing amount of darkness. We studied the association of seasons with sleep variables using linear regression analysis and, in addition, studied the interac‐ tion of the genetic variant and seasons by adding an interaction term to the linear regression. Stratified analyses for the association of sea‐ sons with sleep were performed in the rs12506228 genotype groups. 2.2.3 | Genotyping An umbilical cord blood sample was drawn from each newborn at birth. DNA was extracted according to standard procedures at the National Institute for Health and Welfare. Genotyping of the rs12506228 variant TA B L E 1  Key values of the main variables at 8 months of age by genotypes of rs12506228 Total sample rs12506228 CC rs12506228 CA rs12506228 AA Na  Skewness Kurtosis Na  Mean SD Na  Mean SD Na  Mean SD Total sleep time (hr) 1,102 −0.26 0.65 554 13.3 1.14 463 13.3 1.15 85 13.3 1.2 Diurnal/total sleep time 1,102 0.51 0.59 554 0.26 0.07 463 0.26 0.06 85 0.26 0.08 Night awakenings 1,033 0.65 −0.23 519 1.9 1.4 432 2.0 1.4 82 1.9 1.4 Socioemotional development 1,123 −0.33 −0.06 568 21.0 2.4 456 20.7 2.3 90 20.5 2.4 Communication development 1,116 −0.11 −0.41 563 13.7 3.6 463 13.3 3.4 90 12.6 3.3 Motor development 1,116 0.08 −0.92 563 15.2 4.1 465 15.0 4.0 88 15.1 4.2 Actigraphy noc‐ turnal sleep time (hr) 315 −0.32 0.77 142 8.6 0.94 147 8.7 0.96 26 8.4 0.92 aIndividuals with genetic data and age between 7 months and 10 months. F I G U R E 2  Associations of rs12506228 across the lifespan. The variant near melatonin receptor 1A gene (MTNR1A), rs12506228, was first discovered as a top finding of GWAS and replication studies for job‐related exhaustion in shift‐workers (Sulkava et al., 2017). Subsequently, a strong association with clinical and neuropathological Alzheimer's disease was detected in old cohorts (Sulkava et al., 2018). Here, we demonstrated an association of the same risk variant with slower early cognitive development, as well as with decreased seasonal variation in infants’ sleep. As the variant has an association with lower brain expression of MTNR1A (Sulkava et al., 2017), we suggest that the diverse associations of rs12506228 across the lifespan are mediated by weaker melatonin signalling due to a relative lack of receptors, which may cause different phenotypes at different ages. Molecular mechanisms mediating these phenotypes in the carriers of the risk allele may include the following: an increase in the rhythm‐changing effect of light at night (Sulkava et al., 2017), increased processing of APP to amyloid (Sulkava et al., 2018), reduced neuroprotection during foetal brain development, and decreased transmission of the seasonal changes in the amount of melatonin      |  5 of 9SULKAVA et AL. was based on genome‐wide genetic mapping with the Illumina Infinium PsychArray BeadChip, which comprises 603,132 single‐nucleotide polymorphisms (SNPs). Genotyping was processed at the Estonian Genome Centre, University of Tartu. Quality control was performed using PLINK 1.9 ( http://www.cog‐genom ics.org/plink/ 1.9/; Purcell et al., 2007). Markers were removed for missingness (> 5%) and Hardy– Weinberg equilibrium (p‐value < 1 × 10−6). Individuals were checked for missing genotypes (> 5%), relatedness (identical by descent calculation, PI_HAT > 0.2) and population stratification (multidimensional scaling). 2.3 | Statistical analysis All the data were analysed with SPSS (IBM SPSS Statistics for Windows, version 25, IBM, Armonk, NY, USA). We used linear re‐ gression models in all of the analyses, with age and gender as co‐ variates. An additive model for genetic association analyses was used, meaning that the genotypes were coded based on the num‐ ber of the minor allele A in rs12506228 (CC = 0, AC = 1, AA = 2). To enable the use of a linear regression model in our statisti‐ cal analysis, we tested the normality of the variables by observing skewness and kurtosis. Kurtosis and skewness values between −0.92 and 0.77 were detected (Table 1). Because a linear regression model is not sensitive for small deviations from normality (Jupiter, 2017), all the variables could be used in the model without trans‐ formation. A nominal p‐value of .05 was used as the threshold of significance in all the analyses. In addition, we used FDR correction for our six main variables (socioemotional, communication, gross motor development, total sleep time, diurnal sleep time/total sleep time, and night awakenings) using the FDR online calculator (FDR online calculator www.sdmpr oject.com/utili ties/?show=FDR, n.d.). The FDR correction is, however, likely to be a rather conservative correction because of the correlation of the traits (e.g. Pearson correlation coefficient for socioemotional and communication development = .44, and for total sleep time and ratio of diurnal sleep/total sleep time = −.35). Therefore, a significance level of .1 was selected. 3  | RESULTS 3.1 | Association of rs12506228 with sleep traits The additive effects of the rs12506228 genetic variant on sleep were analysed with linear regression models adjusted for age and gender. No significant associations were detected with sleep length or night awakenings in the questionnaire data or nocturnal sleep length measured with actigraphy (Table 2). 3.2 | Association of rs12506228 with child development We detected an association of rs12506228 with cognitive develop‐ ment: in the additive model, the minor allele A of rs12506228 was associated with slower socioemotional (p = .025, p‐FDR = 0.075, beta = −0.24) and communication development (p = .0098, p‐ FDR = 0.059, beta = −0.42). No significant association with gross motor development was detected. The results can be examined in Table 2. Adjusting the model with potential environmental factors affecting development and sleep (poor family environment, mater‐ nal depression, maternal age, breastfeeding, number of siblings, birth season) did not change the associations of rs12506228 (Table S2). 3.3 | Association of seasons with sleep traits and interaction with rs12506228 We also examined the seasonal differences in infant sleep by ana‐ lysing the association between the season during which the assess‐ ment occurred and sleep traits. The results of the linear regression analyses showed that the questionnaire‐based total sleep time as well as the actigraphy‐based nocturnal actual sleep time were sig‐ nificantly longer during the darker months of the year (i.e. in win‐ ter). Moreover, night awakenings were more abundant in the darker months and the proportion of diurnal sleep from the total sleep time was higher. These results are presented in Table 3. TA B L E 2  Association of rs12506228 with development and sleep traits N Beta SE p‐value Total sleep time (hr) 1,102 0.030 0.055 .58 Diurnal/total sleep time 1,102 0.001 0.003 .84 Night awakenings 1,033 0.019 0.069 .78 Socioemotional development 1,123 −0.24 0.11 .025*  Communication development 1,116 −0.42 0.16 .0098*  Motor development 1,116 0.009 0.19 .96 Actigraphy nocturnal sleep time (hr) 312 0.007 0.085 .94 Age and gender used as covariates in the analyses. Bold values, nominally significant association with p < .05. *Significant with FDR correction p < .1. TA B L E 3  Association of seasons of examination with sleep traits N Beta SE p‐value Total sleep time (hr) 1,102 0.18 0.051 .00033 Diurnal/total sleep time 1,102 0.008 0.003 .0070 Night awakenings 1,033 0.16 0.064 .014 Actigraphy nocturnal sleep time (hr) 349 0.15 0.075 .048 Note: Covariates of age and gender used in the analyses. Winter coded 3, spring and fall coded 2, summer coded 1. Bold values, nominally significant association with p < .05. 6 of 9  |     SULKAVA et AL. We then examined an interaction term for season and rs12506228 in predicting the sleep traits. The interaction term was significant for the actigraphy‐measured sleep length (p = .041, beta = −0.25), and a trend was visible for the questionnaire‐based sleep length (p = .063, beta = −0.15). Diurnal/total sleep time ratio and night awakenings did not show significant interaction terms (p = .77 and .97). In addi‐ tion to examining the interaction term, we performed stratified anal‐ yses to show the effects of the seasons on sleep separately within the genotype groups of rs12506228. Regarding sleep length (ques‐ tionnaire‐based and actigraphy), a significant association of seasons with sleep was detected among carriers of the CC genotype (p = .01, beta = 0.24 and p = .016, beta = 0.29) and, for the questionnaire‐ based trait, a weaker association among the carriers of the AC gen‐ otype was visible (p = .018, beta = 0.19). Among carriers of the AA genotype, no significant association of seasons with any sleep trait was detected and the beta coefficient was to the opposite direction. Thus, we showed that the association of seasons on sleep length was not visible among carriers of the AA genotype of rs12506228 and, when compared with the CC group, it was weaker among the carriers of the AC genotype. The results of the association of seasons with the sleep traits by genotype groups are presented in Table 4, and examples of actograms for different seasons and different genotype groups are shown in Figure 1. 4  | DISCUSSION Variant rs12506228 near the MTNR1A gene has previously been connected to sensitivity to circadian disruption as well as Alzheimer's disease (Sulkava et al., 2017, 2018). In this study, we examined the association of the variant with infants’ sleep and development at the age of 8 months. We found that the previous risk allele (minor allele A) of rs12506228 was associated with slower socioemotional and communication development. No direct association with sleep traits was detected; however, rs12506228 was found to regulate the as‐ sociation of seasons on infants’ sleep length (Figure 2). The A allele of rs12506228 is potentially linked to lower expres‐ sion of MTNR1A in the brain (Sulkava et al., 2017). Based on this, we anticipated that carrying the minor allele A of rs12506228 would have negative effects on the development of sleep due to weaker melatonin signalling. However, we could not find support for this hypothesis as no connection between the genetic variant and sleep traits was detected. Our results show that seasons affect sleep traits: darker months of the year were associated with longer total sleep time (both question‐ naire‐based and actigraphy‐measured), a higher number of night awak‐ enings, and a higher proportion of diurnal sleep from the total sleep time. This may reflect weaker consolidation of nocturnal sleep in infants. The study subjects were living in the Pirkanmaa region of Finland, in latitude 61°N where considerable seasonal changes in day length occur. Lengthening of the total sleep time during winter has previously been observed in the adult population (Cepeda et al., 2018); however, a large questionnaire‐based population study from Norway did not find this effect (Sivertsen, Øverland, Krokstad, & Mykletun, 2011). In school‐aged children, a small increase in the sleep length has been suggested to occur during the winter months (Hjorth et al., 2013). In line with this finding, a study of 5‐ to 12‐year‐old Finnish children showed less actigraphy‐measured activity during the darker months (Aronen, Fjallberg, Paavonen, & Soininen, 2002). A study of 34 infants aged 7 months did not show seasonal differences in sleep length or the number of awakenings (Cohen, Atun‐einy, & Scher, 2012). However, this could be due to lack of power or less variation in day length as the study was carried out in Israel. In a large study of 1‐month‐old infants in the southern part of Japan, longer sleep time was observed in those born in spring compared with those born in autumn (Iwata et al., 2017). Interestingly, the seasonal differences in sleep length we ob‐ served in our study seem to be dependent on the genotype of rs12506228. We found that the seasonal differences are not visible among carriers of the AA genotype of rs12506228. Longer sleep time during the Finnish winter could be caused by higher melatonin levels (Adamsson, Laike, & Morita, 2016; Kivelä, Kauppila, Ylöstalo, Vakkuri, & Leppäluoto, 1988), by the weaker direct activating effect of light, or by other seasonally dependent factors, such as tempera‐ ture or the amount of outdoor activity. As the A allele of rs12506228 is linked to fewer MT1 melatonin receptors, our interaction results support the assumption that the seasonal effects on sleep length are caused by changes in melatonin levels. Our findings confirm the hypothesis regarding developmental aspects: the A allele of rs12506228 was associated with slower so‐ cioemotional and communication development, but not with motor TA B L E 4  Association of seasons with sleep traits in the genotype groups of rs12506228 Trait rs12506228 CC rs12506228 CA rs12506228 AA N Beta SE p‐value N Beta SE p‐value N Beta SE p‐value Total sleep time 554 0.24 0.07 .001 463 0.19 0.08 .018 85 −0.25 0.20 .20 Diurnal/total sleep time 554 0.005 0.004 .203 463 0.013 0.004 .0036 85 −0.002 0.013 .89 Night awakenings 519 0.122 0.09 .17 432 0.27 0.10 .010 82 −0.07 0.21 .74 Actigraphy noctur‐ nal sleep time 142 0.29 0.12 .016 146 0.15 0.12 .20 26 −0.25 0.28 .38 Note: Age and gender used as covariates in the analyses. Winter coded 3, spring and fall coded 2, summer coded 1. Bold values, nominally significant association with p < .05.      |  7 of 9SULKAVA et AL. development in the first 8 months of life. The association may be caused by a lower amount of melatonin receptor type 1A in the brain, which was previously linked to the A allele of rs12506228 (Sulkava et al., 2017). During the foetal period, melatonin and the melatonin receptor may play an important role in the development of the brain: in this period MT1 melatonin receptors are, unlike in adults, abun‐ dant in a number of discrete brain areas (Thomas, Purvis, Drew, Abramovich, & Williams, 2002). Infants’ own melatonin production from the pineal gland begins post partum, but maternal melatonin, which is present in high levels and has a clear circadian rhythm at the end of pregnancy (Kivela, 1991), crosses the placenta freely (Reppert, Chez, Anderson, & Klein, 1979) and is capable of syn‐ chronising foetal biological rhythms (Mendez et al., 2012) through these receptors. Animal models have suggested that melatonin has a pre‐ and perinatal neuroprotective effect against hypoxic‐ischaemic brain injury (Robertson et al., 2013), which could therefore protect consecutive cognitive development. In ovine models and in a pilot study in humans, melatonin has also been found to protect against intra‐uterine growth restriction (Miller et al., 2014), a multifactorial condition linked to problems in neurocognitive development in in‐ fancy and childhood (Levine et al., 2015). The association with early development is interesting when considering the previously demonstrated strong association of rs12506228 with clinical Alzheimer's disease, and the amyloid and neurofibrillary pathology of Alzheimer's disease (Sulkava et al., 2018). Previously, studies on the strongest risk gene for sporadic late‐onset Alzheimer's disease, APOE, have suggested expression of the genetic risk of Alzheimer's disease in childhood. In a large imag‐ ing study of 3‐ to 20‐year‐old participants, carriers of APOE geno‐ types ɛ4ɛ4, ɛ2ɛ4 and ɛ2ɛ2 showed an association with hippocampal volume and age‐dependent brain maturation in ways that resemble the changes in Alzheimer's disease. The brain volume changes also correlated with poorer performance in attention tasks and working memory (Chang et al., 2016). In another study, magnetic resonance imaging changes in APOE ε4 carriers were detected in 2‐ to 25‐ month‐old children (Dean et al., 2014). Differences in grey matter volume in APOE ɛ4 carriers have been reported already in neonates (Knickmeyer et al., 2014). In contrast, association with faster mental development has been suggested in 24‐month‐old carriers of the ɛ4 risk genotype (Wright et al., 2003). Some studies have also exam‐ ined another Alzheimer's risk gene, APP, and reported association of the risk SNPs with verbal and total level of intelligence in 8‐year‐old children (Myrum, Nikolaienko, Bramham, Haavik, & Zayats, 2017). Our findings suggest that not only genes related to APP metabolism, but also other genes associated with Alzheimer disease risk, here MTNR1A, could manifest as early as childhood. 4.1 | Limitations Our study has some limitations to consider when interpreting the findings. Firstly, estimating an infant's nocturnal sleep is affected by many parent‐related factors, such as their own ability to sleep and bed‐ sharing with an infant. Secondly, parental evaluations of their infant's development are vulnerable to subjective biases, even though the as‐ pects of development used in the questionnaires were planned to be easily detectable by parents. Thirdly, the actigraphy data were consid‐ erably smaller (n = 314) than the questionnaire sleep data (n = 1,103), although the actigraphy data managed to support our findings based on the questionnaire. Finally, the seasonal assessments were con‐ ducted on different infants studied in different seasons in contrast to following up the same infant. However, it must be stated that this pre‐ vents the aging of the infants from affecting the results. 5  | CONCLUSIONS In summary, we did not detect a direct association between the MTNR1A variant and sleep variables, but we showed that the darker months associate with longer sleep length only in the absence of the AA genotype of rs12506228, which is possibly linked to fewer MT1 mela‐ tonin receptors. We also showed an association between rs12506228 and slower socioemotional and communication development during the first 8 months of infancy, which may be linked to the importance of melatonin signalling during foetal development. These results may lead to a better understanding of different phenotypic expressions of genetic risks of adult conditions in the developing brain. However, to elucidate this more, longitudinal research across the lifespan is needed. ACKNOWLEDG EMENTS The authors received funding for this project from the Academy of Finland (grants 134880; www.aka.fi/skidi‐kids, 253346 to T.P., 308588 to E.J.P.), Foundation for Pediatric Research (to E.J.P.), the Gyllenberg Foundation (to T.P.), the Maud Kuistila Memorial Foundation (to S.S.), and the Emil Aaltonen Foundation (to S.S.). The authors would like to thank Prof. Debra Skene for helpful discussions on foetal melatonin, and Auli Toivola for coordinating the sample collection. The authors wish to acknowledge the CSC–IT Center for Science, Finland, for computational resources. CONFLIC T OF INTERE S T No conflicts of interest declared. AUTHOR CONTRIBUTIONS S.S. analysed the data, contributed to the design of the current study and wrote the manuscript. A.‐M.T. analysed the data and wrote the manuscript. K.K. analysed the data and drafted the manuscript. E.J.P. designed the cohort collection and drafted the manuscript. O.S.‐H. designed the cohort collection and drafted the manuscript. P.P. designed the cohort collection and drafted the manuscript. I.M.‐M. prepared and scored the actigraphy data, produced Figure 1 and drafted the manuscript. L.M. contributed to the genotyping and drafted the manuscript, T.P.‐H. designed the cohort collection and drafted the manuscript. A.K. designed the cohort collection and 8 of 9  |     SULKAVA et AL. drafted the manuscript. T.P. supervised the study, contributed to the design of the current study and of the cohort collection, and drafted the manuscript. 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