Introduction
The field of endophenotype research has identified several laboratory-based biobehavioral measures that index the genetic variance related to psychopathology and psychological traits (Anokhin, Reference Anokhin2014; Gottesman & Gould, Reference Gottesman and Gould2003; Gottesman & Shields, Reference Gottesman and Shields1972; Kendler & Neale, Reference Kendler and Neale2010). As detailed in a recent review of psychophysiological endophenotypes (Iacono, Malone, & Vrieze, Reference Iacono, Malone and Vrieze2017), candidate endophenotypes for a variety of psychiatric/psychological phenotypes, such as alcohol/substance use and schizophrenia, include measures of spontaneous (resting state) electroencephalogram (EEG) power, P3 event-related potential (ERP) amplitude and θ and δ oscillatory activity during a target/oddball task, and antisaccade eye-tracking error rate. Prior work has demonstrated that many of these measures show familiarity, that is, they are (a) heritable, (b) are present in close relatives (e.g. offspring or co-twins/siblings) of those with the phenotype or disorder, and (c) share genetic variance with the phenotype/disorder (e.g. overlap between latent genetic factors through biometric modeling of twin/family data) as reviewed in (Anokhin, Reference Anokhin2014; Iacono et al. Reference Iacono, Malone and Vrieze2017). However, for a measure to be considered a ‘full’ (rather than ‘candidate’) endophenotype, it is expected to show associations with identified genetic variants (Iacono et al. Reference Iacono, Malone and Vrieze2017). Despite decades of research following the initial seminal psychiatric endophenotype work of Gottesman and Shields (1972), the ability of candidate endophenotypes to identify robust and reliable specific genetic variants has remained elusive.
Initial conceptualizations of endophenotypes assumed that they were highly heritable but with a simpler genetic architecture than that of psychiatric/psychological phenotypes, which, in theory, would aid substantially in the discovery of associated genes. This does not appear to be the case. We previously evaluated the genetic basis of a collection of promising candidate psychophysiological endophenotypes, including resting-state EEG power; P3 and θ/δ activity; and antisaccade performance, in a community-based sample of over 4800 individuals and failed to find robust and, convincing evidence for, single genes or variants influencing any of the endophenotypes (Iacono, Malone, Vaidyanathan, & Vrieze, Reference Iacono, Malone, Vaidyanathan and Vrieze2014; Malone et al. Reference Malone, Burwell, Vaidyanathan, Miller, McGue and Iacono2014a, Reference Malone, Vaidyanathan, Basu, Miller, McGue and Iacono2014b; Malone, McGue, & Iacono, Reference Malone, McGue and Iacono2017; Vaidyanathan et al. Reference Vaidyanathan, Malone, Donnelly, Hammer, Miller, McGue and Iacono2014; Vrieze et al. Reference Vrieze, Malone, Vaidyanathan, Kwong, Kang, Zhan and Iacono2014b). Work using meta-analytic techniques and analysis of analog biobehavioral measures in model organisms (Flint & Munafò, Reference Flint and Munafò2007) supports the notion that the effect sizes for genetic variants contributing to endophenotypes are not in fact larger than those contributing to other psychiatric/psychological traits. Like other complex traits, endophenotypes commonly used in behavioral sciences appear to be highly polygenic in nature and at present may not be helpful for discovering genetic variants with large effects.
A promising, yet underappreciated, the utility of endophenotypes is their ability to characterize biological mechanisms related to psychological phenotypes and the genetic variants of these phenotypes as identified through large-scale genome-wide association studies (GWAS). Recent advances in molecular genetics provide evidence that common variants with small effect sizes additively contribute to the phenotypic expression of many psychiatric disorders and psychological traits (Bogdan, Baranger, & Agrawal, Reference Bogdan, Baranger and Agrawal2018). Rather than testing single genes or variants in isolation, polygenic scores (PGS), also known as polygenic risk scores, are a way to model the aggregate influence across genetic variants associated with a phenotype. PGS are calculated by weighting single-nucleotide polymorphisms (SNPs) by the strength of their association with a phenotype (e.g. schizophrenia). Although endophenotypes may not strongly relate to single genes or variants, they may prove useful in understanding the biobehavioral mechanisms of aggregate polygenic liability.
In that vein, our previous paper (Liu et al. Reference Liu, Malone, Vaidyanathan, Keller, Abecasis, McGue and Vrieze2017) focused on testing the one-to-one association between candidate endophenotypes and a PGS based on a large schizophrenia GWAS meta-analysis by the Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014). Despite relatively well-powered tests, we found no significant correlations after multiple comparison adjustment.
The predictive strength of a PGS is highly dependent on a strong and well-powered discovery GWAS to provide accurate and precise weights for the SNP-phenotype association (Dudbridge, Reference Dudbridge2013); the lack of significant findings in our previous work may have been in part due to PGS imprecision. Increasingly, well-powered summary statistics identifying more significant reliable effect alleles for a phenotype may improve the statistical power of a PGS to relate to biobehavioral mechanisms. Moreover, while our previous report focused on schizophrenia polygenic risk, it is unknown whether endophenotypes index the polygenic liability for other psychological phenotypes, such as substance use (Liu et al. Reference Liu, Jiang, Wedow, Li, Brazel, Chen and Vrieze2019; Pasman et al. Reference Pasman, Verweij, Gerring, Stringer, Sanchez-Roige, Treur and Vink2018) or educational attainment (Lee et al. Reference Lee, Wedow, Okbay, Kong, Maghzian, Zacher and Cesarini2018). A ‘multivariate endophenotype’ approach, leveraging the covariation across many endophenotypes using statistical techniques like principal component analysis (PCA), may also improve power to detect polygenic effects (Gilmore, Malone, & Iacono, Reference Gilmore, Malone and Iacono2010; Harper, Malone, & Iacono, Reference Harper, Malone and Iacono2019a; Iacono, Carlson, & Malone, Reference Iacono, Carlson and Malone2000; Jones et al. Reference Jones, Porjesz, Chorlian, Rangaswamy, Kamarajan, Padmanabhapillai and Begleiter2006; Price et al. Reference Price, Michie, Johnston, Innes-Brown, Kent, Clissa and Jablensky2006). Multivariate endophenotypes have potentially greater explanatory power than any single endophenotype because they combine the unique and shared (genetic) variance explained across many endophenotypes and help capture the multiple biological and cognitive pathways giving rise to a single phenotype (Gottesman & Gould, Reference Gottesman and Gould2003).
In the current study, we sought to extend our previous work (Liu et al. Reference Liu, Malone, Vaidyanathan, Keller, Abecasis, McGue and Vrieze2017) by performing a comprehensive test on the association between multivariate endophenotypes and a range of PGSs, including alcohol/nicotine/cannabis use [the most commonly used substances in the United States; (Substance Abuse and Mental Health Services Administration, 2020)], educational attainment, and an updated PGS for schizophrenia in a large community-based sample of over 4800 individuals from the Minnesota Twin Family Study (MTFS). PCA-based multivariate endophenotypes were derived from 11 psychophysiological measures with reasonable construct validity as candidate endophenotypes for phenotypes of interest (see the subsection ‘Endophenotypes’ of section ‘Methods’), namely, P3 amplitude, band-limited resting-state EEG power, and antisaccade performance [i.e. those studied in Liu et al. (Reference Liu, Malone, Vaidyanathan, Keller, Abecasis, McGue and Vrieze2017)], plus additional measures of δ and θ oscillatory activity elicited during a visual oddball task (Malone et al. Reference Malone, McGue and Iacono2017). PGS were derived from the summary statistics of four recent largest of their kind GWAS meta-analyses: (1) alcohol (drinks per week) and nicotine (regular smoking initiation) use (Liu et al. Reference Liu, Jiang, Wedow, Li, Brazel, Chen and Vrieze2019); (2) lifetime cannabis use (Pasman et al. Reference Pasman, Verweij, Gerring, Stringer, Sanchez-Roige, Treur and Vink2018); (3) years of educational attainment (Lee et al. Reference Lee, Wedow, Okbay, Kong, Maghzian, Zacher and Cesarini2018); and (4) an updated GWAS meta-analysis of schizophrenia (Pardiñas et al. Reference Pardiñas, Holmans, Pocklington, Escott-Price, Ripke, Carrera and Walters2018). PGSs were calculated using a novel approach, LDPred (Vilhjálmsson et al. Reference Vilhjálmsson, Yang, Finucane, Gusev, Lindström, Ripke and Price2015), which takes into account the linkage disequilibrium between markers and is a potentially more powerful analytic tool than the traditional PGS calculation approach used in our previous report. To verify the appropriateness of this sample to evaluate PGS-endophenotype relationships, additional tests evaluated measurement construct validity (e.g. heritability of psychophysiological measures, significant associations between PGSs for phenotypes with previously demonstrated genetic correlations). Significant findings would provide important information regarding potential biobehavioral mechanisms related to the polygenic architecture of these psychiatric/psychological phenotypes.
Methods
Participants
Participants were assessed as part of the Minnesota Center for Twin and Family Research (MCTFR), a community-based sample of twins and their parents. The reader is referred to our previous papers for extensive details on this sample and the endophenotypes used here (Iacono et al. Reference Iacono, Malone, Vaidyanathan and Vrieze2014; Liu et al. Reference Liu, Malone, Vaidyanathan, Keller, Abecasis, McGue and Vrieze2017; Malone et al. Reference Malone, McGue and Iacono2017). Participants completed a battery of psychiatric assessments, self-report questionnaires, and behavioral/psychophysiological laboratory-based tests (Iacono et al. Reference Iacono, Malone, Vaidyanathan and Vrieze2014, Reference Iacono, Malone and Vrieze2017; Iacono & McGue, Reference Iacono and McGue2002; Keyes et al. Reference Keyes, Malone, Elkins, Legrand, McGue and Iacono2009; Wilson et al. Reference Wilson, Haroian, Iacono, Krueger, Lee, Luciana and Vrieze2019). Participants were genotyped on the Illumina 660W-Quad as described previously (Miller et al. Reference Miller, Basu, Cunningham, Eskin, Malone, Oetting and McGue2012; Vrieze et al. Reference Vrieze, Feng, Miller, Hicks, Pankratz, Abecasis and McGue2014a) and then imputed to the Haplotype Reference Consortium (McCarthy et al. Reference McCarthy, Das, Kretzschmar, Delaneau, Wood and Teumer2016) panel using the Michigan imputation server (Das et al. Reference Das, Forer, Schönherr, Sidore, Locke, Kwong and Fuchsberger2016). The number of individuals with genotypes and at least one psychophysiological measure was 4905. We selected individuals primarily of European descent for the current report by calculating four principal components (PCs) on the European population in the 1000 G (1000 Genomes Project Consortium et al. Reference Auton, Brooks, Durbin, Garrison, Kang and Abecasis2015) using PLINK (Chang et al. Reference Chang, Chow, Tellier, Vattikuti, Purcell and Lee2015), projecting the MCTFR genotypes on the resulting PC weights, and selecting participants who fell within the space defined by the 1000 Genomes EUR-ancestry individuals; this resulted in a total sample of 4893 individuals.
Endophenotypes
The endophenotypes have been described in detail elsewhere (Iacono et al. Reference Iacono, Malone, Vaidyanathan and Vrieze2014; Liu et al. Reference Liu, Malone, Vaidyanathan, Keller, Abecasis, McGue and Vrieze2017; Malone et al. Reference Malone, McGue and Iacono2017; Vrieze et al. Reference Vrieze, Malone, Vaidyanathan, Kwong, Kang, Zhan and Iacono2014b). All endophenotypes were corrected for sex, age, assessment cohort, and 10 PCs reflecting the major dimensions of genetic variation in this sample, and, for EEG measures, recording system. A brief overview is as follows.
Event-related EEG
Participants completed a rotated heads visual oddball task (Begleiter, Porjesz, Bihari, & Kissin, Reference Begleiter, Porjesz, Bihari and Kissin1984) during EEG recording. We focused on EEG activity to target/oddball stimuli. P3 amplitude was calculated from the trial-averaged ERP across all target trials at midline parietal electrode Pz. Given the strong evidence that P3 is not a unitary phenomenon but rather a mixture of superimposed δ and θ frequency-band activity (Karakas, Erzengin, & Basar, Reference Karakas, Erzengin and Basar2000a, Reference Karakas, Erzengin and Basar2000b; Kolev, Demiralp, Yordanova, Ademoglu, & Isoglu-Alkaç, Reference Kolev, Demiralp, Yordanova, Ademoglu and Isoglu-Alkaç1997), we calculated four additional measures [not examined in our prior report (Liu et al. Reference Liu, Malone, Vaidyanathan, Keller, Abecasis, McGue and Vrieze2017)]. As described in (Malone et al. Reference Malone, McGue and Iacono2017), we calculated time–frequency energy (total power) and inter-trial phase clustering/coherence (ITPC: a measure of the consistency of the EEG oscillatory signal across trials) for θ (at frontal midline electrode Fz) and δ (at parietal midline electrode Pz). P3 amplitude reduction is reliably associated with schizophrenia and several forms of substance use, including alcohol, nicotine, and cannabis (Anokhin et al. Reference Anokhin, Vedeniapin, Sirevaag, Bauer, O'Connor, Kuperman and Rohrbaugh2000; Bramon, Rabe-Hesketh, Sham, Murray, & Frangou, Reference Bramon, Rabe-Hesketh, Sham, Murray and Frangou2004; Euser et al. Reference Euser, Arends, Evans, Greaves-Lord, Huizink and Franken2012; Iacono & Malone, Reference Iacono and Malone2011; Iacono et al. Reference Iacono, Malone and Vrieze2017; Solowij, Michie, & Fox, Reference Solowij, Michie and Fox1991). Reduced θ and δ energies and ITPC are also associated with substance use and schizophrenia (Burwell, Malone, Bernat, & Iacono, Reference Burwell, Malone, Bernat and Iacono2014; Ethridge et al. Reference Ethridge, Hamm, Pearlson, Tamminga, Sweeney, Keshavan and Clementz2015, Reference Ethridge, Hamm, Shapiro, Summerfelt, Keedy, Stevens and Clementz2012; Ford, Roach, Hoffman, & Mathalon, Reference Ford, Roach, Hoffman and Mathalon2008; Harper, Malone, & Iacono, Reference Harper, Malone and Iacono2019b; Jones et al. Reference Jones, Porjesz, Chorlian, Rangaswamy, Kamarajan, Padmanabhapillai and Begleiter2006; Rangaswamy et al. Reference Rangaswamy, Jones, Porjesz, Chorlian, Padmanabhapillai, Kamarajan and Begleiter2007; Yoon, Malone, Burwell, Bernat, & Iacono, Reference Yoon, Malone, Burwell, Bernat and Iacono2013).
Resting-state EEG power
EEG was recorded while participants were asked to relax with eyes closed for 5 min and listen to soft white noise. We obtained power in δ, θ, α, and β frequency bands via a fast Fourier transformation of EEG at the central midline electrode Cz (averaged bilateral earlobe signal reference). Alpha power was also calculated from the average of two bipolar parieto-occipital derivations (O1-P7 and O2-P8). Individual differences in resting-state power have been linked to schizophrenia (Hong, Summerfelt, Mitchell, O'Donnell, & Thaker, Reference Hong, Summerfelt, Mitchell, O'Donnell and Thaker2012; Kam, Bolbecker, O'Donnell, Hetrick, & Brenner, Reference Kam, Bolbecker, O'Donnell, Hetrick and Brenner2013; Narayanan et al. Reference Narayanan, O'Neil, Berwise, Stevens, Calhoun, Clementz and Pearlson2014; Venables, Bernat, & Sponheim, Reference Venables, Bernat and Sponheim2009), alcohol dependence (Kamarajan, Pandey, Chorlian, & Porjesz, Reference Kamarajan, Pandey, Chorlian and Porjesz2015; Rangaswamy & Porjesz, Reference Rangaswamy and Porjesz2014), smoking (Rass, Ahn, & O'Donnell, Reference Rass, Ahn and O'Donnell2016; Su et al. Reference Su, Yu, Cheng, Chen, Zhang, Guan and Yuan2017), cannabis use (Ehlers, Phillips, Gizer, Gilder, & Wilhelmsen, Reference Ehlers, Phillips, Gizer, Gilder and Wilhelmsen2010; Herning, Better, Tate, & Cadet, Reference Herning, Better, Tate and Cadet2003; Struve et al. Reference Struve, Straumanis, Patrick, Leavitt, Manno and Manno1999), externalizing (Rudo-Hutt, Reference Rudo-Hutt2015), and intelligence quotient (Langer et al. Reference Langer, Pedroni, Gianotti, Hänggi, Knoch and Jäncke2012; Posthuma, Neale, Boomsma, & de Geus, Reference Posthuma, Neale, Boomsma and de Geus2001; Thatcher, North, & Biver, Reference Thatcher, North and Biver2005).
Eye tracking
Participants were asked to fixate on a dot in the center of a computer screen. At variable intervals, a second dot was flashed to either side of the screen and participants were instructed to look in the opposite direction. The antisaccade measure is the proportion of trials in which the individual looked toward the light rather than away from it (failure to inhibit their prepotent response). Several studies have suggested antisaccade error rate as an endophenotype for schizophrenia (Calkins, Curtis, Iacono, & Grove, Reference Calkins, Curtis, Iacono and Grove2004; Calkins, Iacono, & Ones, Reference Calkins, Iacono and Ones2008; Levy, Mendell, & Holzman, Reference Levy, Mendell and Holzman2004; McDowell et al. Reference McDowell, Brown, Paulus, Martinez, Stewart, Dubowitz and Braff2002; Radant et al. Reference Radant, Dobie, Calkins, Olincy, Braff, Cadenhead and Tsuang2010).
Creation of PGS
Summary statistics for drinks per week and regular smoking initiation (binary phenotype of ever being a regular smoker in one's lifetime, coded as 0 = no and 1 = yes) were obtained from the GWAS and Sequencing Consortium of Alcohol and Nicotine (GSCAN) use; see (Liu et al. Reference Liu, Jiang, Wedow, Li, Brazel, Chen and Vrieze2019) for details.Footnote 1 Summary statistics from the largest GWAS meta-analysis of lifetime cannabis use (binary phenotype of having ever used cannabis, coded as 0 = no and 1 = yes) were obtained (Pasman et al. Reference Pasman, Verweij, Gerring, Stringer, Sanchez-Roige, Treur and Vink2018). Summary statistics for schizophrenia were obtained from a recent GWAS meta-analysis of schizophrenia, the largest of its kind to date (https://walters.psycm.cf.ac.uk/) (Pardiñas et al. Reference Pardiñas, Holmans, Pocklington, Escott-Price, Ripke, Carrera and Walters2018). Summary statistics for educational attainment were obtained from the largest to date GWAS meta-analysis of educational attainment (Lee et al. Reference Lee, Wedow, Okbay, Kong, Maghzian, Zacher and Cesarini2018). MCTFR was one of the discovery cohorts in the GSCAN, cannabis, and educational attainment GWAS meta-analyses and, as such, was not included in the set of summary statistics used to create the PGS here.
The final PGS for drinks per week (discovery N = 937 381) contained 1 093 636 variants, regular smoking initiation (discovery N = 1 225 910; 52.0% of which were cases on average across all meta-analyzed studies) contained 1 093 640 variants, lifetime ever use of cannabis (discovery N = 184 765; 28.8% cases) contained 805 738 variants, schizophrenia (discovery N = 105 318; 38.6% cases) contained 1 073 315 variants, and educational attainment (discovery N = 762 526) contained 1 093 298 variants.
PGSs were calculated using LDPred (Vilhjálmsson et al. Reference Vilhjálmsson, Yang, Finucane, Gusev, Lindström, Ripke and Price2015), a Bayesian method of PGS calculation that estimates posterior mean causal effect sizes from GWAS summary statistics conditioning on a point-normal mixture distribution for the genetic architecture of effects and a reference sample for LD patterns. MCTFR genotypes were pruned to only those with imputation quality score R2> 0.7, then further limited to variants with minor allele frequency > 0.01 and present in HapMap3 as these reflect the vast majority of common genetic variance and are extensively vetted variants with stable and well-known properties. LDPred was used to calculate beta weights for variants of all significance levels (p ⩽ 1). Individual PGS were then calculated in PLINK 1.9 (Chang et al. Reference Chang, Chow, Tellier, Vattikuti, Purcell and Lee2015).
Statistical analysis
All statistical analyses were conducted in R (R Core Team, 2019).
First, we calculated twin/family correlations and twin-based heritability (Boker et al. Reference Boker, Neale, Maes, Wilde, Spiegel, Brick and Fox2011) for each endophenotype. Next, we examined the correlations between PGSs to both evaluate the covariation among the PGS for each phenotype and ensure that the calculated PGSs behave as expected (e.g. substance use PGS relate to each other).
To calculate multivariate endophenotypes, PCA [psych R package (Revelle, Reference Revelle2020)] was used to exploit the covariation among endophenotypes and extract a smaller number of meaningful dimensions/components for statistical analysis. Analyzing PCs that account for most of the variance of the observed endophenotypes also reduces the burden of multiple testing relative to testing each of the 11 endophenotypes individually. As a preliminary step prior to PCA, missing endophenotype data was imputed with the regularized iterative PCA algorithm [missMDA R package (Josse & Husson, Reference Josse and Husson2016)] using generalized cross-validation to empirically choose the most appropriate imputation method (Josse & Husson, Reference Josse and Husson2012). The algorithm indicated mean imputation as the most appropriate, which has been shown to be a viable option for moderately correlated variables [r ~ 0.30; (Dray & Josse, Reference Dray and Josse2015)], such as those in the current study (see online Supplementary Fig. S1). We note that the pattern of results was identical when using the pairwise correlation approach (using all complete pairs of observations) to deal with missing data (results not shown). Parallel analysis using both resampled and simulated data (2000 iterations) were used to determine the number of components. Components were retained if the actual data eigenvalue was greater than the corresponding simulated/resampled data eigenvalue. The component structure was obliquely rotated (Promax) to facilitate interpretation and scores were calculated for statistical analyses.
The main analyses of interest, that is, testing the association between each PGS (independent variable) and each multivariate endophenotype PC (dependent variable) were calculated using a rapid feasible generalized least-squares (RFGLS) regression method [RFGLS R package (Li, Basu, Miller, Iacono, & McGue, Reference Li, Basu, Miller, Iacono and McGue2011)] to account for dependency among parents, monozygotic (MZ) twins and dizygotic (DZ) twins and calculate appropriate standard errors in the presence of clustered data. To evaluate uncertainty around effect sizes and determine the significance of the standardized beta (β) estimates, we used the car R package (Fox & Weisberg, Reference Fox and Weisberg2019) to conduct nonparametric residual bootstrapping (5000 random draws) of the regression models and the boot package (Canty & Ripley, Reference Canty and Ripley2021) to compute bias-corrected and accelerated 95% confidence intervals (CIs) [for a technical discussion, see (van der Leeden, Meijer, & Busing, Reference van der Leeden, Meijer, Busing, J. and E.2007)].
Results
Endophenotype descriptions
Table 1 contains descriptive statistics for each endophenotype. In all cases, the within-family mother–father correlations were negligible and offspring–parent correlations were small. Endophenotypes were moderately to strongly heritable, as evidenced by MZ correlations being at least approximately twice the DZ correlations and the twin-based heritability point estimates.
Table 1. Summary statistics for the endophenotypes
Off., offspring; MZ, monozygotic; DZ, dizygotic.
Associations among PGSs
A pattern of shared genetic liability was observed across the PGSs, as shown in Table 2. As expected, the three substance use PGSs were positively correlated, and all were positively correlated with the schizophrenia PGS. The education attainment PGS was negatively correlated with the regular smoking PGS but positively correlated with the cannabis use PGS, a pattern consistent with other studies (Pasman et al. Reference Pasman, Verweij, Gerring, Stringer, Sanchez-Roige, Treur and Vink2018; Wedow et al. Reference Wedow, Zacher, Huibregtse, Harris, Domingue and Boardman2018).
Table 2. Correlations among PGSs
Notes: Nonparametric bootstrap 95% confidence intervals are presented under correlation point estimates. Intervals that did not overlap with zero were considered significant and are bolded.
Notably, the correlation between drinks per week and regular smoking PGS was in the same direction as the genetic correlations between these phenotypes [as reported previously; (Liu et al. Reference Liu, Jiang, Wedow, Li, Brazel, Chen and Vrieze2019)], albeit lower in magnitude. This is expected since the present correlations reflect the covariance between PGSs, not the latent additive genetic covariance between two phenotypes, and supports the construct validity of the PGSs.
PCA-based multivariate endophenotypes
The parallel analysis supported extracting four components explaining a total of 72% of the variance across endophenotypes (Fig. 1) with each respective component explaining 21, 20, 19, and 11% of the variance. Component loadings are shown in Fig. 1; loadings ≥|0.40| were used to interpret the PCs (i.e. multivariate endophenotypes). PC1 primarily indexed low-frequency power (strongest loadings: resting-state δ/θ power), PC2 captured high-frequency power (strongest loadings: resting-state α/β power), PC3 indexed event-related P3/δ (strongest loadings: P3, δ energy, and δ ITPC), and PC4 primarily captured endophenotypes related to prefrontal control (strongest loadings: antisaccade error rate, θ ITPC, and θ energy). θ energy cross-loaded on PC1 and PC4. All four multivariate endophenotypes were heritable (Table 3).
Fig. 1. Left. Scree plots of the principal component analysis (PCA) eigenvalues estimated from the actual (observed) data and eigenvalues from two forms of parallel analysis (simulated and resampled data). The plot provides empirical support for retaining four PCs as the actual data eigenvalues were greater than the simulated/resampled eigenvalue for components 1–4 but not 5. The gray line along the y-axis demarcates the traditional Kaiser's eigenvalues greater than one rule, which also supports four components. Right. Profile plots of the component loadings (Promax oblique rotation) for each endophenotype on PCs 1–4. Loadings >|0.40| (illustrated by the dashed line) were used in the interpretation of the components; endophenotypes with loadings ≥|0.40| are indicated in bold on the x-axis. ITPC, intertrial phase consistency.
Table 3. Within-family correlations and twin heritability estimates for the multivariate endophenotypes
PC, principal component; Off., offspring; MZ, monozygotic; DZ, dizygotic.
Associations between PGSs and multivariate endophenotypes
The association between multivariate endophenotypes and PGSs are presented in Table 4. Four associations were statistically significant. Polygenic risk for schizophrenia was negatively predictive of PC3 scores (i.e. the event-related P3/δ component). Three significant associations were observed for the prefrontal control PC4 (θ/antisaccade): the drinks per week and regular smoking PGSs were negatively associated with PC4 scores, whereas the educational attainment PGS was positively associated with PC4 scoresFootnote 2 No significant associations were observed for the low- or high-frequency resting-state power multivariate endophenotypes or the cannabis use PGS.
Table 4. Associations between multivariate endophenotypes and PGSs
PC, principal component.
Notes: Standardized beta estimates (β) with nonparametric bootstrap 95% confidence intervals (CIs) that did not overlap with zero were considered significant and are bolded. Using the formula for generalized least squares proposed by Buse (Reference Buse1973), the R 2 for significant effects ranged from 0.10% for PC3 and regular smoking/educational attainment to 0.12% for PC3-schizophrenia and PC4-drinks per week.
Discussion
In this current report, we substantially extended our previous work by testing the relationship between multivariate endophenotypes and up-to-date PGSs spanning multiple domains. Our previous investigation, designed to test the individual predictive utility of 8 of the current 11 endophenotypes with a schizophrenia PGS constructed using weights from a relatively large schizophrenia GWAS meta-analysis (Schizophrenia Working Group of the Psychiatric Genomics Consortium, 2014) was unsuccessful (Liu et al. Reference Liu, Malone, Vaidyanathan, Keller, Abecasis, McGue and Vrieze2017). In the current report, we adopted several approaches aimed at increasing the power to identify significant PGS–endophenotype associations. We utilized up-to-date PGSs for alcohol use, regular smoking, cannabis use, schizophrenia, and educational attainment calculated with well-powered GWAS meta-analysis summary statistics (Lee et al. Reference Lee, Wedow, Okbay, Kong, Maghzian, Zacher and Cesarini2018, Reference Liu, Jiang, Wedow, Li, Brazel, Chen and Vrieze2019; Pardiñas et al. Reference Pardiñas, Holmans, Pocklington, Escott-Price, Ripke, Carrera and Walters2018; Pasman et al. Reference Pasman, Verweij, Gerring, Stringer, Sanchez-Roige, Treur and Vink2018) and more sensitive statistical methods (LDPred), which should in theory produce more robust PGSs with greater reliability and explanatory power relative to our prior report. We also examined additional candidate endophenotypes not tested in our previous report (i.e. δ/θ energy and ITPC). Finally, rather than testing each endophenotype in isolation, a PCA-based multivariate endophenotype approach was used to leverage the combined explanatory influence across 11 candidate endophenotypes. We observed significant associations between PGSs and multivariate endophenotypes reflecting event-related EEG activity and prefrontal control-related endophenotypes. Specifically, an event-related P3/δ component was negatively associated with schizophrenia PGS, whereas a prefrontal control component was negatively related to drinks per week and regular smoking PGSs but positively associated with educational attainment PGS. In contrast, no significant effects were found for multivariate endophenotypes of low- or high-frequency resting-state power or the cannabis use PGS. Findings offer novel preliminary evidence linking psychophysiological multivariate endophenotypes to polygenic liability in psychiatric/psychological phenotypes.
Within a construct validation framework, aside from simply sharing genetic variance with a phenotype, endophenotypes are expected to show robust associations with specific genetic variants (Gottesman & Gould, Reference Gottesman and Gould2003; Iacono et al. Reference Iacono, Malone and Vrieze2017). The use of endophenotypes to identify single genes/variants related to psychological phenotypes has not yielded much success (Iacono et al. Reference Iacono, Malone and Vrieze2017). In contrast to examining the influence of a single allele, it has been suggested that polygenic approaches may increase the chance of identifying endophenotypes, and therefore potential biobehavioral mechanisms, related to genetic liability for a psychiatric disorder or psychological phenotype (Bogdan et al. Reference Bogdan, Baranger and Agrawal2018). In a similar vein, by jointly leveraging the shared and unique genetic variance across several measures, multivariate endophenotypes may better index the multiple biological and cognitive risk pathways influencing a phenotype than any single endophenotype alone (Frederick & Iacono, Reference Frederick and Iacono2006; Gottesman & Gould, Reference Gottesman and Gould2003). Informed by these suggestions, we observed several novel findings of statistically significant associations between PGS and multivariate endophenotypes. An event-related parietal P3/δ component was negatively related to schizophrenia polygenic risk. A prefrontal control component indexing event-related frontal θ and antisaccade performance had negative associations with PGSs for both drinks per week and regular smoking initiation, and a positive association with educational attainment PGS. While effect sizes were small in magnitude, we believe that these findings can serve as potentially promising leads for future research using further refined PGS and multivariate endophenotypes in even larger samples, such as the EEG workgroup of the ENIGMA (Enhancing NeuroImaging Genetics through Meta Analysis) consortium (Thompson et al. Reference Thompson, Andreassen, Arias-Vasquez, Bearden, Boedhoe and Brouwer2017), and we discuss their potential implications below.
Polygenic risk for schizophrenia was negatively correlated with a multivariate endophenotype primarily reflective of parietal P3 amplitude and δ energy and ITPC to target stimuli during a visual oddball task. This pattern is consistent with previous literature demonstrating a genetic association between reduced P3 and schizophrenia (Ford, Reference Ford1999; Jeon & Polich, Reference Jeon and Polich2003) and reduced δ energy/ITPC in individuals with schizophrenia (Ford et al. Reference Ford, Roach, Hoffman and Mathalon2008). In neurocognitive terms, P3 and δ activity elicited by rare target detection are correlates of decision-making and signal-matching processes, such as evaluating whether a stimulus classification and the associated chosen response choice is appropriate (Başar-Eroglu, Başar, Demiralp, & Schürmann, Reference Başar-Eroglu, Başar, Demiralp and Schürmann1992; Cooper, Darriba, Karayanidis, & Barcelo, Reference Cooper, Darriba, Karayanidis and Barcelo2016; Harper, Malone, & Iacono, Reference Harper, Malone and Iacono2017; Verleger & Śmigasiewicz, Reference Verleger and Śmigasiewicz2016). Anomalies in these EEG correlates may be part of a constellation of traits associated with polygenic risk for schizophrenia, and if confirmed by future work, offer further support of these measures as endophenotypes for schizophrenia. It should be noted that these effects are in contrast to our previous report that found no significant associations with P3 (Liu et al. Reference Liu, Malone, Vaidyanathan, Keller, Abecasis, McGue and Vrieze2017). This is likely attributable to three key differences in the current report: (1) relative to the PGS used in our previous report, we used an updated PGS from the largest schizophrenia GWAS to date (Pardiñas et al. Reference Pardiñas, Holmans, Pocklington, Escott-Price, Ripke, Carrera and Walters2018) that identified more significant associated loci (145 compared to 108) and explained more variance in schizophrenia liability (5.7% compared to 3.4%); (2) the use of a more powerful PGS calculation analytic tool (LDPred), likely producing a more robust/predictive PGS; and (3) the multivariate endophenotype approach combining several schizophrenia-related endophenotypes, including novel measures of δ energy and ITPC alongside P3.
A multivariate endophenotype capturing prefrontal control-related measures (θ energy and ITPC, antisaccade error rate) was associated with PGS for alcohol consumption, regular smoking, and educational attainment. Frontal θ activity is thought to reflect a reactive mechanism related to successful attentional allocation, orienting, and control-related prefrontal cortex processes (Başar-Eroglu et al. Reference Başar-Eroglu, Başar, Demiralp and Schürmann1992; Cavanagh & Frank, Reference Cavanagh and Frank2014; Clayton, Yeung, & Kadosh, Reference Clayton, Yeung and Kadosh2015; Harper et al. Reference Harper, Malone and Iacono2017), and is a candidate endophenotype for (poly)substance use (Harper et al. Reference Harper, Malone and Iacono2019b; Rangaswamy et al. Reference Rangaswamy, Jones, Porjesz, Chorlian, Padmanabhapillai, Kamarajan and Begleiter2007). Antisaccade performance has been linked to prefrontal inhibitory control (Hutton & Ettinger, Reference Hutton and Ettinger2006), and some evidence suggests it indexes risk for substance use/behavioral disinhibition (Iacono, Reference Iacono1998; Young et al. Reference Young, Friedman, Miyake, Willcutt, Corley, Haberstick and Hewitt2009). The current findings, if confirmed by future research, suggest that individual differences in a multivariate endophenotype related to frontal executive functioning may index the polygenic risk for both regular smoking and the number of alcoholic drinks per week, which may have significant public health implications given the prevalence of alcohol and nicotine use (Substance Abuse and Mental Health Services Administration, 2020). The lack of significant cannabis use PGS effect may reflect a level of differentiation between substances and endophenotypes despite moderate genetic overlap among substances in this sample (Table 2) and others (Jang et al. Reference Jang, Saunders, Liu, Jiang, Liu and Vrieze2020; Liu et al. Reference Liu, Jiang, Wedow, Li, Brazel, Chen and Vrieze2019; Pasman et al. Reference Pasman, Verweij, Gerring, Stringer, Sanchez-Roige, Treur and Vink2018). The cannabis GWAS identified fewer significant variants compared to the similarly sized schizophrenia or much larger alcohol/smoking GWASs; cannabis-related variants might have very small effect sizes (like those for alcohol/smoking) and a significantly larger discovery sample may be needed to improve precision/power of the cannabis PGS to detect endophenotype effects. The prefrontal control multivariate endophenotype was also positively correlated with educational attainment PGS, potentially reflecting an improved ability to deploy frontal attentional and executive control processes in those with a higher polygenic load for completing more years of education.
While not a primary focus of this report, we observed several novel significant cross-trait associations between PGSs. For example, positive correlations were found between schizophrenia, alcohol, regular smoking, and cannabis use PGSs, which suggests a common genetic basis (shared loci) indicative of a general vulnerability toward all four phenotypes (Jang et al. Reference Jang, Saunders, Liu, Jiang, Liu and Vrieze2020). Increased polygenic risk for regular smoking was related to decreased polygenic load for educational attainment, which is interesting given that both were associated (in opposing directions) with the prefrontal control multivariate endophenotype.
We acknowledge that the potential utility of the current findings is limited given the small effect sizes between PGSs and endophenotypes. However, this was unsurprising as the cross-trait explanatory variance of PGSs is often small even for the phenotypes from which they are derived. For example, as reported in the original articles, the phenotypic variance explained by PGSs was 1 and 4% for drinks per week and regular smoking, respectively (Liu et al. Reference Liu, Jiang, Wedow, Li, Brazel, Chen and Vrieze2019) and highest at ~12% for educational attainment (Lee et al. Reference Lee, Wedow, Okbay, Kong, Maghzian, Zacher and Cesarini2018). The relationship between the PGS and a brain measure is likely expected to be even smaller, as distant cross-domain correlations are expected to be lower than closely related within-domain measures (Campbell & Fiske, Reference Campbell and Fiske1960), consistent with what was found here. The significant PGS-multivariate endophenotype associations observed in this report all had absolute standardized β estimates of ~0.03, explaining 0.10–0.12% of the variance (Buse, Reference Buse1973), whereas PGS–PGS associations explained up to 5.66%. As suggested in guidelines proposed in a recent review on effect sizes in psychological science (Funder & Ozer, Reference Funder and Ozer2019), small effect sizes found in large samples are to be expected, can have large downstream causal effects, and are likely more believable than the inverse. It may be that other psychophysiological measures or further refined PGS with even larger discovery samples and stronger analytic methods may explain more production of larger effect sizes, but this remains to be seen. Nevertheless, these findings show that progress is being made in linking endophenotypes to specific polygenic influences. We note that endophenotypes have additional potential utility beyond identifying specific PGS links, such as prospectively predicting phenotypic expression (e.g. substance use initiation, see Anokhin and Golosheykin, Reference Anokhin and Golosheykin2016; Harper et al. Reference Harper, Malone and Iacono2019a) or informing brain mechanisms that may potentially identify system-level targets for treatment responses or environmental interactions (Bogdan et al. Reference Bogdan, Baranger and Agrawal2018; Iacono et al. Reference Iacono, Malone and Vrieze2017) in a similar fashion as to how GWAS has helped understand how certain tissues or genes may be associated with a trait.
Another potential limitation may be PGS imprecision. By aggregating across many variants not associated with the endophenotype but rather only with the phenotype, PGSs may contain ‘noise’ that may downwardly bias its association with an endophenotype. Another issue relevant to the current state of GWAS research is the lack of a large sample non-European GWAS meta-analyses. The GWAS meta-analyses statistics used here are all based on individuals of European descent, thus limiting the target population to only Europeans. More research is needed in non-European populations to generalize the effectiveness of both the PGS, which may vary in part by ancestral allele frequency differences, and endophenotypes to better understanding the biological underpinnings of these complex traits.
The current results represent meaningful progress in linking polygenic liability for schizophrenia, alcohol use, regular smoking, and educational attainment to multivariate psychophysiological endophenotypes of decision-making (P3/δ) and prefrontal control (θ/antisaccade). While at present endophenotypes may not explain large amounts of polygenic variance in psychopathology or psychological traits, these results are an encouraging step forward. Future studies will likely benefit from leveraging a large collection of relevant endophenotypes, each likely accounting for a small amount of variance, in a multivariate fashion to better understand the polygenic risk associated with a single trait.
Supplementary material
The supplementary material for this article can be found at https://doi.org/10.1017/S0033291721000763.
Acknowledgements
This research was supported through National Institutes of Health grants DA037904, DA044283, DA042755, DA013240, DA040177, DA041120, DA036216, DA005147, AA009367, and DA024417. JH was supported by the University of Minnesota Eva O. Miller Fellowship and the National Institute on Drug Abuse of the National Institutes of Health under Award Number T32DA037183. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
This report is based on work completed by the second author (ML) in partial fulfillment of the requirements for the degree of Doctor of Philosophy, under the supervision of the senior author (SIV). Preliminary findings related to this manuscript appeared in a poster presentation at the 48th Annual Meeting of the Behavior Genetics Association (June 2018).
The authors acknowledge the Minnesota Supercomputing Institute (MSI) at the University of Minnesota for providing resources that contributed to the research results reported within this paper. URL: http://www.msi.umn.edu.
We thank the anonymous reviewers for their helpful comments on an earlier version of this article. Finally, we extend our gratitude to the families for their participation in our studies.
Conflicts of interest
None.
Ethical standards
The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.
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