Introduction

Depression is a common mental health disorder and predicted to be the highest global burden of disease by 2030 (WHO, 2012). Adolescence marks a period where depressive symptoms increase and major depressive disorder will commonly onset (Kessler, Avenevoli, & Merikangas, 2001; Kessler et al., 2005; Malhi & Mann, 2018; Rohde, Lewinsohn, Klein, Seeley, & Gau, 2013). Adolescent depressive symptoms and major depressive disorder are associated with a number of psychiatric and social impairments in later life and show strong continuity with depression in adult life, thus making it important to prevent and treat (Copeland, Shanahan, Costello, & Angold, 2009; Copeland, Wolke, Shanahan, & Costello, 2015; Fergusson, Boden, & Horwood, 2007; Rutter, Kim-Cohen, & Maughan, 2006).

Depression has a complex and multifactorial aetiology, comprised of both environmental and genetic contributions (Flint & Kendler, 2014; Thapar, Collinshaw, Pine, & Thapar, 2012). Adult twin studies have estimated that the heritability of major depressive disorder is between 31% and 42% (Sullivan, Neale, & Kendler, 2000). Twin studies of adolescent depressive symptoms have estimated similar heritability to adult depression, with a range most likely between 30% and 50%, with lower estimates reported for symptoms during childhood (Glowinksi, Madden, Bucholz, Lynskey, & Heath, 2003; Rice, 2014). However, there is considerable variability in heritability estimates between studies (11–72%) depending on age, sex, informant and measure of assessment (Rice, 2009). Depressive symptomatology typically increases during adolescence, and twin studies have shown that genetic contributions influence this developmental change (Hannigan, Walaker, Waszczuk, McAdams, & Eley, 2017). In particular, genetic contributions may increase throughout development (Bergen, Gardner, & Kendler, 2007; Rice, Harold, & Thapar, 2002) although some inconsistent results have also been reported (Nivard et al., 2015). There are strong continuities reported between adolescent and adult depression (Rutter et al., 2006), so inconsistent estimates of heritability for adolescent depressive symptomatology are puzzling and may result from between-study differences in measurement, informant and age. Longitudinal data spanning transitions from early adolescence and young adulthood using the same assessments and respondents over time would aid understanding of the nature of genetic influences on the onset and persistence of symptoms across development.

Recent advances in genome-wide association studies (GWAS) have provided evidence that common genetic variation plays a role in depression (Howard et al., 2018, 2019; Wray et al., 2018) with many genetic variants or single-nucleotide polymorphisms (SNPs) each having a small effect (Mullins & Lewis, 2017). The heritability of depression estimated from SNPs is ˜9% in adults (Howard et al., 2019) and ˜2% in adolescents (Jami et al., 2020). However, this low SNP heritability in adolescence partly reflects a smaller GWAS sample size used and the varying informants and greater measurement heterogeneity. Polygenic risk scores (PRS), which sum the number of ‘risk’ SNPs that an individual possesses for a trait weighted by their effect size (Martin, Daly, Robinson, Hyman, & Neale, 2018), can be used as an indicator of an individual’s genetic liability to depression. Several studies have used PRS taken from GWAS of psychiatric traits in adult populations to investigate how they associate with depressive symptomatology across development in younger populations (Halldorsdottir et al., 2019; Kwong, López-López, et al., 2019; Rice et al., 2018; Riglin et al., 2018). One study found that a higher PRS for major depressive disorder (MDD) was associated with depression in both clinical and population cohorts of children and adolescents (Halldorsdottir et al., 2019), whilst another found that the influence of an MDD PRS on emotional problems increased with age with weaker effects in childhood which developed in adulthood (Riglin et al., 2018). The same study also found that a higher PRS for schizophrenia was associated with higher emotional problems in childhood. Similar results were observed in a separate study which found that a greater PRS for MDD was associated with both early and later adolescent-onset trajectories of depression, whilst a higher schizophrenia PRS was only associated with an early adolescent-onset trajectory (Rice et al., 2018). Together, these studies highlight that polygenic risk is likely to play a role in the development and maintenance of adolescent depression. Additionally, other traits such as anxiety and neuroticism are genetically correlated with depression (Luciano et al., 2018; Purves et al., 2020), implying shared genetic aetiology underlying these traits. However, it is still unclear if these genetic factors that show correlations with depression (i.e. anxiety; neuroticism), also uniformly impact on how depression manifests across development. Alternatively, there may be differential effects on developmental depression that are specific to each genetic factor/liability to a trait. Examining how and when PRS for different traits impact on depression developmentally could enhance our understanding of the mechanisms underlying depression and how this varies across developmental stages.

There is evidence that PRS are associated with changes across childhood and adolescence for other traits such as height (Paternoster et al., 2011) and BMI (Khera et al., 2019; Warrington, Howe, et al., 2013). These studies have used a repeated measures framework that estimates trajectories or growth curves to examine genetic associations with changes in a trait. Using a repeated measures framework such as growth-curve modelling may help improve the statistical power of genetic analysis (Lubke et al., 2016). Measurement error and low statistical power are problems in genomic analysis as genetic effects tend to be small in magnitude and require large sample sizes with precision to detect true effects (Hatoum, Rhee, Corley, Hewitt, & Friedman, 2018). Likewise, variation in the reported genetic component for depression may be partially a result of differential phenotypic measurement error at different occasions (Rice, 2014). A longitudinal approach which uses repeated measures may reduce phenotypic measurement error and increase statistical power as there are multiple occasions included in the analysis, rather than just one occasion (Taylor, Simpkin, Haycock, Dudbridge, & Zuccolo, 2016). Indeed, a recent study using an MDD PRS found higher heritability and variance explained by the PRS when utilising a repeated measures framework (Cheesman, 2018). Thus, multiple measurements may obtain a more precise estimate of an individual’s ‘true’ latent trait score as the assessment is repeated over time, and not just on one occasion. Using repeated measurements, it is also possible to reduce the burden for multiple testing that would occur when looking at associations across timings in a growth-curve setting as a number of multiple comparisons are reduced (Warrington, Wu, et al., 2013). Repeated measures analysis, in particular growth-curve modelling, may provide an advantage to traditional cross-sectional analysis and also quantify how and when a trait changes over time, which in this context could help further explain the role of genetic liability in how and when adolescent depression changes over time.

The aim of this study was to examine how genetic liability for five genetically correlated psychiatric traits (as indexed by PRS) influenced depressive symptoms across adolescence and early adult life using cross-sectional and repeated measures designs. Specifically, we aimed to test how PRS for depression (DEP; aka broad depression), major depressive disorder (MDD), anxiety (ANX), neuroticism (NEU) and schizophrenia (SCZ) were all associated with both the initial level and the rate of change of depressive symptoms over this developmental risk period. We conducted the following analyses: (a) we created five PRS taken from recent GWAS (using clumping and thresholding methods) and examined univariate associations at nine occasions in a UK-based population cohort between the ages of 10 and 24 years old (cross-sectional analysis); (b) we then used growth-curve modelling to construct trajectories of depressive symptoms in the same cohort and examined how the five PRS were associated with the rate of change in depressive symptoms throughout adolescent development; and (c) finally, we examined if higher PRS for each trait were associated with differences in depressive symptoms scores compared to the population average PRS in order to determine when each PRS was most strongly associated with depressive symptomology.

Methods Sample

We used data from the Avon Longitudinal Study of Parents and Children (ALSPAC), a longitudinal cohort study that recruited pregnant women residing in the former area of Avon, UK, with expected dates of delivery 1st April 1991 to 31st December 1992 (Boyd et al., 2013; Fraser et al., 2013). The initial cohort consisted of 14,062 live births, but has been increased to 14,901 children who were alive after one year with further recruitment (Northstone et al., 2019). Ethical approval was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees. The study website contains details of all the data that are available through a fully searchable data dictionary and variable search tool: http://www.bristol.ac.uk/alspac/researchers/our-data. A flow diagram highlighting the study sample is given in Figure S1.

Depressive symptoms

Self-reported depressive symptoms were measured on nine occasions between ages 10 and 24 using the Short Mood and Feelings Questionnaire (SMFQ) (Angold, Costello, Messer, & Pickles, 1995). The SMFQ is a 13-item questionnaire that measures the presence of depression symptoms in the previous two weeks and was administered via postal questionnaire or in research clinics. Each item is scored between 0 and 2, resulting in a summed score between 0 and 26. See Table 1 for the means, age range and alpha scores for each SMFQ assessment. The SMFQ correlates highly (r = .58) with clinical depression (Thapar & McGuffin, 1998; Turner, Joinson, Peters, Wiles, & Lewis, 2014).

Table 1. Descriptive statistics of the Short Mood and Feelings Questionnaire (SMFQ) for individuals included in this analysis Occasion (Total N) Mean age Mean SMFQ Alpha % Above SMFQ thresholda 1 (N = 5,317) 10.63 (0.25) 4.00 (3.49) .80 5.85 2 (N = 4,923) 12.80 (0.23) 3.94 (3.84) .84 7.11 3 (N = 4,492) 13.83 (0.21) 4.92 (4.49) .86 11.81 4 (N = 3,521) 16.68 (0.24) 5.85 (5.63) .91 17.68 5 (N = 3,210) 17.82 (0.37) 6.50 (5.21) .90 21.05 6 (N = 2,387) 18.64 (0.49) 6.73 (5.85) .91 21.20 7 (N = 2,377) 21.94 (0.52) 5.56 (5.46) .91 17.44 8 (N = 2,704) 22.87 (0.51) 6.07 (5.40) .90 17.64 9 (N = 2,737) 23.88 (0.51) 6.84 (5.91) .91 23.49 Standard deviations are given in (parenthesis). a Scores equal to or above 11 have been proposed as good indicators of depression (Turner et al., 2014). Individuals included in this analysis had data on the SMFQ, genetic data to make the PRS/principal components of ancestry and data on sex. Alpha scores were estimated using Cronbach’s alpha. Polygenic risk scores

Five PRS were created with PRSice-2 (Choi & O'Reilly, 2019), using summary statistics from five recent genome-wide association studies (GWAS): depression (aka broad depression) or DEP (Howard et al., 2019), major depressive disorder or MDD (Wray et al., 2018), anxiety or ANX (Purves et al., 2020), neuroticism or NEU (Luciano et al., 2018), and schizophrenia or SCZ (Schizophrenia Working Group of the Psychiatric Genomics Consortium, Ripke, Walters, & O'Donovan, 2020). ALSPAC was not included in any of these GWAS. All PRS were created by weighting the effect sizes of the single-nucleotide polymorphisms (SNPs) associated with each trait from the initial GWAS at nine p-value thresholds (PT: 5 × 10−08, 5 × 10−07, 5 × 10−06, 5 × 10−05, .0005, .005, .05, .5 and 1). The number of SNPs included at each of these PRS thresholds is given in Table S1. Each PRS was standardised to have a mean of 0 and a standard deviation of 1; thus, a higher PRS represents higher genetic liability to each trait. We included SNPs that had a MAF of >1% and info score of >80% and excluded SNPs with an R2 of >0.1 if they were within 250 kb of each other. This was to account for linkage disequilibrium (LD) so that only the most strongly associated SNPs from each region were retained. Complete genotyping information is available in the Supporting Information.

Statistical analysis

For the cross-sectional analyses, regression was performed within PRSice-2 to examine the association between each of the five PRS and depressive symptoms at each of the nine occasions. p-values were corrected using the Benjamini–Yekutieli method for multiple testing (pBY) due to the number of related tests (five PRS × nine PRS thresholds × nine occasions of depressive symptoms [405 tests]). Empirical p-values (with 1,000 permutations) were also calculated to examine within PRS associations between the nine occasions of depressive symptoms at each of the nine PRS thresholds (e.g. DEP PRS × nine PRS thresholds × nine occasions of depressive symptoms). Sex and the first 10 principal components of ancestry were included as covariates.

For the repeated measures analysis, trajectories of depressive symptoms were estimated using multilevel growth-curve modelling (Hedeker & Gibbons, 2006; Raudenbush & Bryk, 2002). Briefly, multilevel growth-curve models create population averaged trajectories with intercept and slope terms. Individual-level trajectories then vary around this population average (i.e. each person can have their own trajectory, with their own intercept and slope that can deviate from the population average). Previous analysis of these data has shown that changes in depressive symptoms over time are nonlinear (Edwards et al., 2014; Kwong, Manley, et al., 2019), with depressive symptoms rising until the age of about 18, then decreasing until around the age of 22, before rising again towards the age of 24. To model these nonlinear trajectories, a multilevel quartic growth-curve polynomial model was chosen. This model contains five key parameters: the intercept, the linear age term, the quadratic age term, the cubic age term and the quartic age term. These age terms allow for nonlinearity in the trajectory and changes in depressive symptoms. Previous research using these data to estimate multilevel growth curves has found higher order polynomials best fitted the data (Kwong, Manley, et al., 2019). We further assessed the fit of this model using information criteria and likelihood ratio tests, consistent with other studies using multilevel growth-curve models (Singer & Willett, 2003) – see Tables S2 and S3, and Figure S2.

To examine how each PRS was associated with changes in depressive symptoms, we included a main effect of each standardised PRS and an interaction of the PRS with each of the fixed-effects age polynomial terms (i.e. linear, quadratic, cubic and quartic age terms). Age was grand-mean centred to 16.53 years (the mean age of all assessments) in order to improve interpretation, since model intercept and intercept variance then broadly correspond to the middle of adolescence (Rawana & Morgan, 2014). The intercept and four polynomial age terms were allowed to vary randomly across individuals to capture each individual’s unique trajectory (i.e. a random intercept and random slopes model). Each psychiatric PRS was run univariately, and further information regarding model fit and model equations are given in the Supporting Information.

To assess the association between each psychiatric PRS and development of symptoms over time, we created a population average trajectory which was comprised of the mean PRS for the sample, a trajectory associated with lower genetic liability (1 SD below mean in PRS) and one trajectory associated with higher genetic liability (1 SD above the mean in PRS). We calculated the predicted depressive symptoms scores at each of the following ages: 10.63, 12.80, 13.83, 16.68, 17.82, 18.64, 21.94, 22.87 and 23.88 (to coincide with the mean ages at which the SMFQ was assessed at each of the nine occasions) for the population average, greater liability, and lower liability PRS trajectories. We then compared the predicted depressive symptoms scores at each of these ages between the population average trajectory and the higher genetic risk PRS trajectory (i.e. to compare greater genetic risk for each PRS compared to the population average). Further information on how these were calculated for the trajectories is presented elsewhere (Kwong, Maddalena, Croft, Heron, & Leckie, 2019). Briefly, the depressive symptoms scores were calculated at each age for the two trajectories (i.e. depressive symptom scores at age 12.80 for the population mean and then for the higher PRS [+1SD] trajectory). The delta method (which incorporates the estimate, standard errors and confidence intervals) was then used to compare these two scores revealing a predicted difference in scores that are derived estimates from each trajectory – thus utilising the repeated nature of the measures to obtain a more accurate estimate. p-values were corrected for using false discovery rate (pFDR). Stata code for our analysis can be found here: https://github.com/kwongsiufung/prs-trajectories. Repeated measures analyses were conducted using Stata 15 (StataCorp, College Station, TX, USA), using the user-written runmlwin command (Leckie & Charlton, 2013), which calls the standalone multilevel modelling package MLwiN v3.01 (www.cmm.bristol.ac.uk/MLwiN/index.shtml). These analyses were adjusted for sex and the first ten principal components of ancestry.

Missing data

Missing data in the trajectories analysis were handled using full information maximum likelihood estimation (FIML) (Curran & Hussong, 2003). Briefly, this assumes that the probability of an individual missing a measure of depressive symptoms does not depend on their underlying depressive symptoms score at that occasion, given their observed depressive symptoms trajectory at other occasions. We included individuals into our analysis if they had at least one measurement of depression symptoms in order to maximise power (Lopez-Lopez et al., 2019). Further information regarding the characteristics of this sample has been described in other work (Kwong, 2019). Previous research on these data has shown that trajectory shapes and characteristics do not vary when comparing individuals with at least one or at least 4 measurements of depressive symptoms (Kwong, Manley, et al., 2019).

Sensitivity analyses

We conducted a variety of sensitivity analyses, as follows. (a) To ensure any benefits of the repeated measures analyses were not due to well-powered GWAS, we ran sensitivity analyses substituting our five psychiatric PRS with two well-powered traits: educational attainment (EA) and height (HEI). We supplemented this by further examining the impact of the five psychiatric PRS on trajectories of height. (b) Given the complexity of our growth curve and the risk over fitting the model, we also estimated a simpler quadratic trajectory with each of the five PRS and calculated the predicted scores for different trajectories in a similar manner to the main analyses. (c) We conducted tests for measurement invariance for depressive symptoms over time (highlighted in Figure S3). (d) We ran analysis exploring the association between the DEP PRS and missing data patterns (shown in Figure S4). Further information regarding these sensitivity analyses can be found in the Supporting Information.

Results Sample characteristics

Of the original 14,901 children alive after one year, 9,399 had at least one measurement of depressive symptoms and 7,877 had genotype data that passed quality control (see the Supporting Information). For the cross-sectional analysis, data were available for 5,317 individuals with a measurement of depressive symptoms at age 10.63 and genotype data, decreasing to 2,737 at age 23.88 (see Table S4). For the repeated measures analysis, data were available for 6,302 individuals with at least one measurement of depressive symptoms, sex and genotype data.

Cross-sectional associations between polygenic risk scores and depressive symptoms

Figure 1 shows a summary of results from the five psychiatric PRS across all nine ages of depressive symptoms, using the best predictive threshold for each PRS. For the associations between each PRS and depressive symptoms, we present timings that correspond to measures in three key periods early adolescence, late adolescence and early adulthood for brevity. Full estimates and the best predictive PRS for each trait at each age are given in Tables S5–S19. A higher PRS for DEP was associated with higher depressive symptoms across all nine occasions (Tables S5–S7). Effect sizes and the amount of variance explained by the DEP PRS generally increased throughout development with smaller estimates at age 10.63 (β = .208, 95%CIs = 0.116, 0.301, R2 = 0.37%, pBY = .0004) compared to age 17.82 (β = .583, 95%CIs = 0.410, 0.757, R2 = 1.30%, pBY = 1.55 × 10−08) and then age 23.88 (β = .864, 95%CIs = 0.650, 1.079, R2 = 2.21%, pBY = 5.29 × 10−12). A similar pattern was observed for the MDD PRS, although the effect sizes and the variance explained were smaller at later ages compared to the DEP PRS (age 10.63: β = .222, 95%CIs = 0.128, 0.317, R2 = 0.40%, pBY = .0002; age 17.82: β = .405, 95%CIs = 0.224, 0.587, R2 = 0.58%, pBY = .0005; age 23.88: β = .646, 95%CIs = 0.430, 0.863, R2 = 1.22%, pBY = 5.86 × 10−07, Tables S8–S10). Effects were less consistent for the ANX PRS, which showed smaller effect sizes and a lower variance explained compared to the DEP PRS, but still evidence of greater effects over time (age 10.63: β = .128, 95%CIs = 0.034, 0.222, R2 = 0.13%, pBY = .109; age 17.82: β = .469, 95%CIs = 0.296, 0.641, R2 = 0.85%, pBY = 6.86 × 10−06; age 23.88: β = .682, 95%CIs = 0.470, 0.894, R2 = 1.41%, pBY = 7.53 × 10−08, Tables S11–S13). The NEU PRS showed comparable associations, effect sizes and variance explained compared to the DEP and MDD PRS at age 10.63 (β = .240, 95%CIs = 0.147, 0.333, R2 = 0.48%, pBY = 2.13 × 10−05) and age 17.82 (β = .559, 95%CIs = 0.385, 0.733, R2 = 1.19%, pBY = 7.53 × 10−08), but the strongest associations of all the PRS at age 23.88 (β = .919, 95%CIs = 0.703, 1.135, R2 = 2.45%, pBY = 3.52 × 10−13, Tables S14–S16). In contrast, the SCZ PRS showed relatively low effect sizes and variance explained compared to the other PRS (age 10.63: β = .112, 95%CIs = 0.019, 0.205, R2 = 0.10%, pBY = .224; age 17.82: β = .271, 95%CIs = 0.094, 0.448, R2 = 0.27%, pBY = .048; age 23.88: β = .292, 95%CIs = 0.073, 0.511, R2 = 0.25%, pBY = .127, see Tables S17–S19).

Cross-sectional analysis between the most predictive PRS for each trait and depressive symptoms across adolescence and early adulthood. ANX, anxiety; DEP, depression (broad depression); MDD, major depressive disorder; NEU, neuroticism; PRS, polygenic risk score; SCZ, schizophrenia. SMFQ, Short Mood and Feelings Questionnaire (depressive symptoms). Analyses were adjusted for sex, age and the first ten principal components of ancestry. The most predictive PRS for each trait had p-value thresholds (PT) that varied between 5 × 10−08 and 1. These are described in full in Supporting Information.* indicates results were significant after Benjamini–Yekutieli correction to control for the number of related multiple tests (405 related tests) Associations between polygenic risk scores and trajectories of depressive symptoms and comparisons between population average PRS trajectories and higher PRS trajectories (+1 SD)

The cross-sectional analyses revealed that on average more liberal PRS thresholds (PT: 0.0005 to 1) explained more of the variance in depressive symptoms across ages compared to more stringent PRS thresholds (PT: 5 × 10−08 to 5 × 10−05, Tables S6, S9, S12, S15 and S18). Consequently, we focused on estimating trajectories with more liberal PRS thresholds for each of the five psychiatric PRS. The best model fit – assessed by lowest deviance – was then used to determine which PRS threshold to compare population average PRS trajectories with higher PRS trajectories (i.e. those with a +1 SD in PRS) for each of the psychiatric PRS traits.

There was evidence that higher psychiatric PRS were associated with greater depressive symptoms scores at the intercept age of 16.53 for the DEP PRS (β = .419, 95%CIs = 0.286, 0.551, p = 5.79 × 10−10), MDD PRS (β = .426, 95%CIs = 0.292, 0.559, p = 4.79 × 10−10), ANX PRS (β = .291, 95%CIs = 0.159, 0.423, p = 1.0 × 10−05), NEU PRS (β = .550, 95%CIs = 0.419, 0.681, p = 2.22 × 10−16) and SCZ PRS (β = .149, 95%CIs = 0.016, 0.283, p = 0.029). However, only the DEP PRS (β = .062, 95%CIs = 0.030, 0.094, p = 0.0001), MDD PRS (β = .064, 95%CIs = 0.032, 0.096, p = 0.0001) and NEU PRS (β = .041, 95%CIs = 0.009, 0.072, p = 0.012) showed consistent evidence for linear change over time. There was no evidence that any of the psychiatric PRS showed meaningful changes over time for the quadratic, cubic and quartic age terms (Tables S20–S24).

All five psychiatric PRS showed that trajectories were higher for those with greater genetic liability to a trait (+1 SD in PRS) compared to those with the PRS population average (Figure 2). However, only the DEP, MDD, ANX and NEU PRS showed predicted differences in depressive symptoms scores that substantially increased across development (Table 2). There was no consistent evidence that the predicted differences in SCZ PRS increased over time as differences tended to remain small and stable. The predicted depressive symptoms scores at each age and for each trajectory are given in Table S25.

Association between the five psychiatric PRS and trajectories of depressive symptoms. ANX, anxiety; DEP, depression (broad depression); MDD, major depressive disorder; NEU, neuroticism; PRS, polygenic risk score; SCZ, schizophrenina; SMFQ, Short Mood and Feelings Questionnaire (depressive symptoms). Averaged population trajectories of depressive symptoms for greater and less genetic liability (±1 SD PRS) for each of the varying PRS compared the population average of the PRS (black line for all). Analyses were adjusted for sex and the first ten principal components of ancestry

Table 2. Predicted mean differences in depressive symptoms scores at varying ages between trajectories for the mean population PRS and for trajectories for each PRS that were +1 SD (i.e. higher genetic risk) DEP (PT = 0.005) MDD (PT = 0.05) ANX (PT = 1) NEU (PT = 0.05) SCZ (PT = 0.0005) EA (PT = 1) HEI (PT = 0.5) Age 10.63 0.21 (0.12, 0.30) 0.22 (0.13, 0.32) 0.13 (0.04, 0.23) 0.19 (0.10, 0.28) 0.09 (−0.01, 0.18) 0.17 (0.08, 0.27) 0.06 (−0.03, 0.15) pFDR = 1.82 × 10−05 pFDR = 8.52 × 10−06 pFDR = .007 pFDR = 9.00 × 10−05 pFDR = .087 pFDR = .0005 pFDR = .259 Age 12.80 0.24 (0.14, 0.34) 0.18 (0.09, 0.28) 0.21 (0.11, 0.30) 0.30 (0.20, 0.39) 0.21 (0.11, 0.31) 0.09 (−0.01, 0.19) 0.01 (−0.08, 0.11) pFDR = 3.83 × 10−06 pFDR = .0003 pFDR = 5.56 × 10−05 pFDR = 7.30 × 10−09 p = 3.82 × 10−05 pFDR = .094 pFDR =0.801 Age 13.83 0.28 (0.17, 0.38) 0.24 (0.13, 0.34) 0.23 (0.13, 0.33) 0.38 (0.28, 0.48) 0.20 (0.10, 0.30) 0.03 (−0.07, 0.13) 0.01 (−0.09, 0.11) pFDR = 2.42 × 10−07 pFDR = 1.18 × 10−05 pFDR = 1.18 × 10−05 pFDR = 8.82 × 10−13 pFDR = .0002 pFDR = .579 pFDR = .871 Age 16.68 0.43 (0.29, 0.56) 0.43 (0.30, 0.57) 0.29 (0.16, 0.43) 0.55 (0.42, 0.69) 0.15 (0.01, 0.28) 0.21 (0.07, 0.34) 0.07 (−0.06, 0.21) pFDR = 1.28 × 10−09 pFDR = 1.13 × 10−09 pFDR = 3.82 × 10−05 pFDR = 2.00 × 10−15 pFDR = .039 pFDR = .003 pFDR = .326 Age 17.82 0.50 (0.36, 0.64) 0.50 (0.35, 0.64) 0.32 (0.18, 0.46) 0.59 (0.45, 0.73) 0.16 (0.02, 0.30) 0.23 (0.09, 0.38) 0.07 (−0.07, 0.21) pFDR = 1.53 × 10−11 pFDR = 4.20 × 10−11 pFDR = 1.70 × 10−05 pFDR = 2.00 × 10−15 pFDR = .038 pFDR = .002 pFDR = 0.389 Age 18.64 0.55 (0.41, 0.70) 0.53 (0.38, 0.67) 0.34 (0.20, 0.48) 0.59 (0.45, 0.74) 0.18 (0.03, 0.32) 0.22 (0.08, 0.36) 0.05 (−0.09, 0.19) pFDR = 2.79 × 10−13 pFDR = 6.20 × 10−12 pFDR = 7.78 × 10−06 pFDR = 2.00 × 10−15 pFDR = .022 pFDR = .004 pFDR = .537 Age 21.94 0.73 (0.56, 0.90) 0.56 (0.39, 0.73) 0.47 (0.30, 0.63) 0.63 (0.46, 0.79) 0.30 (0.13, 0.47) 0.06 (−0.11, 0.23) 0.04 (−0.13, 0.20) pFDR = 2.00 × 10−15 pFDR = 2.54 × 10−10 pFDR = 7.72 × 10−08 pFDR = 6.04 × 10−13 pFDR = .0006 pFDR = .537 pFDR = .676 Age 22.87 0.75 (0.59, 0.91) 0.56 (0.40, 0.72) 0.52 (0.36, 0.68) 0.69 (0.53, 0.85) 0.29 (0.13, 0.45) 0.10 (−0.07, 0.26) 0.02 (−0.14, 0.18) pFDR = 2.00 × 10−15 pFDR = 4.37 × 10−11 pFDR = 4.15 × 10−10 pFDR = 2.00 × 10−15 pFDR = .