Polygenic Score of Subjective Well-Being Is Associated with the Brain Morphology in Superior Temporal Gyrus and Insula

NEUROSCIENCE RESEARCH ARTICLE Li Song et al. / Neuroscience 414 (2019) 210–218

Polygenic Score of Subjective Well-Being Is Associated with the Brain Morphology in Superior Temporal Gyrus and Insula Li Song, a,b,1 Jie Meng, a,b,1 Qiang Liu, a,b Tengbin Huo, a,b Xingxing Zhu, a,b Yiman Li, a,b Zhiting Ren, a,b Xiao Wang a,b and Jiang Qiu a,b,c,* a

Key Laboratory of Cognition and Personality (SWU), Ministry of Education, Chongqing 400715, China

b

School of Psychology, Southwest University (SWU), Chongqing 400715, China

c

Southwest University Branch, Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University, Beijing

100875, China

Abstract—Subjective well-being (SWB) is closely related to our physical and mental health. Existing studies show that neural or genetic basis underpins individual difference in SWB. Moreover, researchers have found high enrichment of SWB-related mutations in the central nervous system, but the relationship between the genetic architecture of SWB and brain morphology has not been explored. Considering the polygenic nature of SWB, in this study, we aim to establish a measure of additive genetic effect on SWB and explore its relationship to the brain anatomical structure. Based on the results of genome-wide association study (GWAS) on SWB, the polygenic scores (PGSs) of SWB at eight different thresholds were calculated in a large Chinese sample (N = 585). Then, we analyzed the associations between the PGSs of SWB and cortical thickness (CT) or gray matter volume (GMV) measured from 3.0-T structural imaging data. In whole-brain analyses, we found that a higher PGS was significantly associated with increased CT in the right superior temporal gyrus (STG) and GMV in the right insula, both of which are involved in social cognition and emotional processing. More importantly, these findings were repeatable at some different thresholds. The results may suggest that the brain morphology of right STG and insula is partly regulated by SWB-related genetic factors. © 2019 IBRO. Published by Elsevier Ltd. All rights reserved. Key words: polygenic score, subjective well-being, cortical thickness, gray matter volume, superior temporal gyrus, insula.

INTRODUCTION

evaluation of the quality of individuals' life according to their self-determined standards, which has three characteristics of subjectivity, stability and integrity (Diener, 2000; Diener et al., 2009). In recent decades, research in behavioral science has found that many factors can affect individual SWB. For instance, Diener et al. (1995) showed that after controlling some additional variables like income, human rights and societal inequality, individualism was still correlated with SWB. Clark et al. (2008) found that there was a strong relationship between economic income and SWB. Moreover, the studies on gender differences in SWB were extensive and confusing. Some studies showed that there was no significant difference between genders (Bartels and Boomsma, 2009; Baetschmann, 2014; Kaliterna and Burusic, 2014), some showed that females were happier than males (Graham and Chattopadhyay, 2013), but others showed that older males were happier than their female counterparts (Pinquart and Sörensen, 2001). What's more, Inglehart (2002) and Plagnol and Easterlin (2008) found that females were happier than males in early adult life, but in

Happiness is an eternal pursuit for almost everyone. The influencing factors of happiness have been a research interest especially in sociology, psychology among other disciplines. According to the findings of Andrews and McKennell (1980), happiness consists of three elements: life satisfaction, positive emotion and lack of negative emotion (high satisfaction, higher positive emotion than negative emotion). Some believed that the essence of happiness is to avoid pain and pursue pleasure (Ryan and Deci, 2001). Subjective wellbeing (SWB) is a small branch of happiness, as the overall *Corresponding author at: Faculty of Psychology, Southwest University, No.2, TianSheng Road, Beibei district, Chongqing 400715, China. Tel.: +86 023- 68367942; fax: + 86 023- 68253304. E-mail address: [email protected] (Jiang Qiu). 1 These authors contributed equally to this work. Abbreviations: PGS, polygenic score; SWB, subjective well-being; CT, cortical thickness; GMV, gray matter volume; GWAS, genome-wide association study; SNP, single nucleotide polymorphism; MRI, magnetic resonance imaging; STG, superior temporal gyrus; ICV, intracranial volume.

https://doi.org/10.1016/j.neuroscience.2019.05.055 0306-4522/© 2019 IBRO. Published by Elsevier Ltd. All rights reserved. 210

Li Song et al. / Neuroscience 414 (2019) 210–218

late adult life this was reversed. In addition, SWB was also related to emotional intelligence (Gallagher and VellaBrodrick, 2008; Kong et al., 2015a; Sánchezálvarez et al., 2016), social support (Chalise et al., 2007; Gallagher and Vella-Brodrick, 2008; Tu and Yang, 2016), self-efficacy (Peng and Shu-Hua, 2005; Strobel et al., 2011), and other elements. Altogether, these studies conclude that SWB is a complex trait and can be influenced by many factors. With the advancement of research methods, brain studies on psychological traits have increasingly been explored. In the past several years, many researchers have analyzed the neural mechanisms of well-being (James and O'Boyle, 2012; Yuan et al., 2012). For instance, Lewis et al. (2014) not only found a significant positive correlation between well-being and gray matter volume (GMV) in the right insula, but also found a significant negative correlation between life goals and GMV in the temporal gyrus. A regional homogeneity (ReHo) study showed that unhappy people had decreased ReHo in the prefrontal cortex (PFC), temporal lobe and retrosplenial cortex than happy people (Luo et al., 2014). Kong et al. (2015b) revealed that well-being was associated with the regional fractional amplitude of low frequency fluctuations (fALFF) in the right posterior superior temporal gyrus (pSTG) and thalamus. Further, they revealed that the intensity of functional connectivity between the thalamus and insula showed a significant negative correlation with well-being (Kong et al., 2015b). Moreover, by reviewing the neuroimaging studies of autobiographical recall, it was shown that happiness had a significant relationship to the activation of many regions including anterior cingulate cortex (ACC), PFC and insula (Suardi et al., 2016). Furthermore, Van't et al. (2017) found a significant relationship between hippocampal volume and SWB. Although the brain regions found in these studies are not completely consistent, they all indicate that there are associations between well-being and brain regions. In addition, SWB not only has a corresponding brain foundation, twin studies show that it is also regulated by genes (Bartels and Boomsma, 2009; Archontaki et al., 2013). Through the meta-analysis of genetic studies on wellbeing, Bartels (2015) established that the heritability of well-being was about 36%. The studies on candidate genes, found out that the serotonin transporter gene (De Neve, 2011), the monoamine oxidase A gene (H. Chen et al., 2013) and the aromatase gene (Yang et al., 2017) had significant associations with SWB. However, many complex phenotypes like SWB are polygenic and are affected by many common genetic variations of small effect. Based on this fact, some researchers used large sample of genome-wide association studies (GWAS) to look for genetic information that has significant association with phenotypes (Gao et al., 2016; Tielbeek et al., 2017; Lee et al., 2018). In the GWAS study on SWB, Okbay et al. (2016) found three significant loci (rs3756290, chr5; rs2075677, chr20; rs4958581, chr5). In subsequent enrichment analysis, not only did they find significant enrichment in the central nervous system, but they also found widespread and complex effects on the brain, endocrine and immune system from the Adrenal and Pancreas

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(Okbay et al., 2016). Afterwards, Turley et al. (2018) introduced a new method, multi-trait analysis of GWAS, and obtained similar results with Okbay et al. (2016). Although Okbay and Turley have found that almost all significant single nucleotide polymorphisms (SNPs) are enriched in the nervous system and speculated that these genetic variations may have effects in the brain (Okbay et al., 2016; Turley et al., 2018), no research has linked the genetic architecture to brain structures with high heritability (for example, Winkler et al. (2010) found that the heritability of cortical thickness (CT) and GMV was 0.691 and 0.723, respectively). Specifically, whether the difference in genes of SWB can be reflected in different morphological indicators of brain calls for deliberations. Thus, this study aims to link the PGS of SWB with the brain structure, and provides a new perspective on understanding SWB. We calculated polygenic scores (PGSs) of SWB based on the results of Okbay and his colleagues (2016) at eight different thresholds and created CT and GMV map for each subject with FreeSurfer. Then, we tested whether the PGSs of SWB were associated with the brain structure. It was assumed that the PGSs of SWB had significant correlations in regions including PFC, STG and insula, as previous studies had shown that these brain regions were directly associated with well-being or were involved in social cognition and emotional processing (Reekum et al., 2007; Heller et al., 2013; Vijayakumar et al., 2014; Kong et al., 2015c; Suardi et al., 2016; Uddin et al., 2017; Caldiroli et al., 2018; Luo et al., 2018).

EXPERIMENTAL PROCEDURES Participants 588 subjects were involved in this study (408 females, 180 males; age-range:16–26 years; mean age:19.46 ± 1.50 years), with both genetic and magnetic resonance imaging (MRI) data. They were healthy undergraduate or graduate Chinese students from Southwest University, China. These subjects were drawn from a longitudinal tracking project, which attempts to explore the associations between genetics, brain imaging and mental health (http://www.qiujlab. com/). All subjects were screened before the experiment to ensure that none of them had a history of mental illness, neurological disorder or cognitive disability. Additionally, no one had received mental health treatment or had a history of substance abuse. All subjects signed informed consent. The study was approved by review board of the center for brain imaging studies at Southwest University.

Genotyping Axypre Blood Genomic DNA Kit (no.11313KC3) was used to extracted 1-2μg genome DNA from 250 μl whole blood. Then, we quantified the concentration of all genome DNA with the Qubit 2.0 Fluorometer (no. Q32866) and the Qubit dsDNA HS Assay Kit (no. Q32854). Further, each sample was genotyped on the Infinium Omni2.5Exome-8 BeadChip according to the manufacturer's protocol. Finally, on the

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basis of fluorescent signal with standard cluster algorithm, we used genotyping module of Genomestudio v3.0 (Illumina, San Diego, CA, USA) to call the genotypes. This is the same as our previous article (Meng et al., 2017). For the next genotype analysis, PLINK versions 1.07 (Purcell et al., 2007) and 1.9 (Chang et al., 2015) were used. First, we executed individual quality control. Individuals with gender mismatches, their missing genotype rates higher than 0.05, their pairwise identity-by-descent (IBD, used to find potentially related individuals) higher than 0.185, and the heterozygosity beyond three standard deviations (SD) were excluded. After that, SNP quality control was carried out. SNPs with the missing genotype rates higher than 0.02, the notable deviation from Hardy–Weinberg Equilibrium (p < 1e-4), and the minor allele frequency (MAF) lower than 0.01 were removed. Next, SNPs in strong linkage disequilibrium (LD > 0.2) were excluded within a window of 50 SNPs and a step of five SNPs using the “indep-pairwise” command in PLINK. Then, we merged our data with HapMap. To control the effects of population stratification, we carried out the smart principal component analysis (PCA) using EIGENSOFT 6.1.3 (Patterson et al., 2006; Price et al., 2006) to exclude population outliers based on PC1 or PC2 (more than 6 SD). Finally, genetic data were imputed using Michigan Imputation Server developed by Das and his colleagues in 2016 (https:// imputationserver.readthedocs.io/en/latest/) (Das et al., 2016). After the data imputation, a quality control was conducted to keep the SNPs with the MAF higher than 0.01, and the INFO (information metric for each reference SNP) score which was used to estimate the imputation quality more than 0.60. After the above processing, 585 subjects with more than 7.2 million SNPs remained for the subsequent analysis.

al., 2012; Power et al., 2015), as Liu et al. stated, these methods do not work in high-dimensional MRI data of our healthy individuals because it is a data driven study with no predefined phenotype. Consequently, in this study, eight different thresholds (0.0001, 0.001, 0.01, 0.05, 0.1, 0.25, 0.5 and 1) were selected to define PGSs. The specific number of variations at each PGS threshold is shown in Table 1.

MRI data acquisition All the brain imaging data were collected using a 3.0-T Siemens Trio MRI scanner (Siemens Medical, Erlangen, Germany). A magnetization prepared rapid gradient echo (MPRAGE) sequence was used to obtain high-resolution T1-weighted anatomical images with the following parameters: repetition time = 1900 ms, flip angle = 9°, echo time = 2.52 ms, inversion time = 900 ms, resolution matrix = 256 × 256, slices = 176, thickness = 1.0 mm, voxel size = 1 × 1 × 1 mm 3.

MRI processing MRI preprocessing was executed by connectome computation system (CCS), an integration system developed by Xu and her colleagues (Xu et al., 2015). This comprised of AFNI (Cox, 2012), FreeSurfer (Fischl, 2012), FSL (Jenkinson et al., 2012) software with Shell and MATLAB scripts. The standard preprocessing was executed by the recon-all pipeline including skull stripping, intensity normalization, Talairach transform computation, white matter segmentation, and brain surface reconstruction and parcellation. After that, surface-based data with GMV (the entire number of structure-based on a defined segmentation) and CT (the average distance linking white matter and pial surfaces) were created (Chen et al., 2015). In this study, we adopted an anatomically effective and reliable automatic labeling system for subdividing the cortical surface (Desikan et al., 2006). To guarantee the quality of the data, various graphics and indicators were created during the preprocessing and two investigators were invited to verify the data. For further analysis, the CT and GMV data were transformed into a common template with a smoothing kernel of 10 mm for both hemispheres.

Calculation of PGS According to the GWAS results from Okbay's study and the PGS computation method developed by Purcell et al. (2009), the “score” parameter in PLINK was used to calculate the PGS of SWB. The selected thresholds for calculating PGSs are not consistent. Generally, a stricter threshold yields a PGS consisting of a fewer SNPs which are actually related to the trait (Sabuncu et al., 2012). However, by increasing the statistical threshold to obtain more truly relevant SNPs, we can improve the effect size of the PGS and thus enhance our statistical power to detect associations with the trait on a finite sample size (Sabuncu et al., 2012). Therefore, researchers chose different thresholds for their own considerations (Holmes et al., 2012; Power et al., 2015; Liu et al., 2016; Lee et al., 2018). Although some methods of threshold estimation have been proposed (Sabuncu et

Assessment of SWB status The Index of Well-Being and The Oxford Happiness Questionnaire (OHQ) were used to evaluate SWB status of our subjects. The Index of Well-Being (Campbell, 1976) consists of two parts: the general affective scale (eight items) and the life satisfaction questionnaire (one item). All items are gradually transitioned from negative emotion to positive emotion, such as “boring (1 2 3 4 5 6 7) interesting”. The total scores of the subjects are the sum of the mean score

Table 1. Number of variations at each p value used for calculating PGS.

PGS (GWAS, p)

0.0001

0.001

0.01

0.05

0.1

0.25

0.5

1

Number of variations

2164

11,276

70,278

270,528

490,502

1,098,660

2,052,512

3,885,538

Note: PGS, polygenic score; GWAS, genome-wide association study.

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PGS and brain structure data is 343 and 458, respectively. A total of 309 subjects have both behavioral scores.

Statistical analysis

Fig. 1. Associations between the PGSs of SWB and CT. (A) Associations between CT and the PGSs at four different thresholds, with gender, age and the ICV as covariates. The CT of red regions showed significant positive correlations (p < 0.05) with the PGSs without correction, but only the brightest region of each brain remained significant after a Monte Carlo simulation with a vertex-wise threshold of p < 0.001 and a cluster-wise threshold of p < 0.05. (B) Scatter plots of the correlations between the significant regions and the PGSs. R values are shown with gender, age and the ICV as covariates. The two gray lines show the 95% confidence intervals of the best-fit lines.Note: PGS0.1 means the PGS of SWB at the threshold of 0.1, PGS0.1*CT means the relationship between PGS0.1 and CT, and so on.

of the general affective scale and the score of the life satisfaction questionnaire. The OHQ (Hills and Argyle, 2002) contains 29 items, and participants rate their level of agreement using a six-point Likert scale from 1 (strongly disagree) to 6 (strongly agree). The sum of all items scores is an overall measure of SWB. In this study, the number of participants with the Index of Well-Being scores or the OHQ scores, the

First, using SPSS 20.0, the Z-values of the eight PGSs of SWB were calculated, and subjects whose Zvalues were beyond 4 SD were eliminated. Then, one independent sample t test was conducted to investigate gender differences in each PGS of SWB. Next, we performed general linear models (GLM) to explore the relationships between the PGSs and the CT or GMV. The PGS of SWB at each threshold was selected as the independent variable, and the CT or GMV was the dependent variable, with gender, age and the intracranial volume (ICV) included as covariates. Moreover, a Monte Carlo simulation was used to control multiple comparisons, with a vertex-wise threshold of p < 0.001 and a clusterwise threshold of p < 0.05. Moreover, the mean CT or GMV values of the brain regions significantly correlated with the PGSs of SWB in the GLM were extracted, and we carried out Pearson correlation analyses to test their relations with the PGSs. Finally, we executed correlation analyses between the behavioral scores and the PGSs or the mean values of the significant brain regions, with gender, age the ICV, PC1 and PC2 included as covariates.

RESULTS General analysis All subjects had Z-valued PGSs within 4 SD. Moreover, the result showed that there were no significant gender

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Table 2. Information on the brain regions where the PGSs of SWB and CT were significantly correlated.

p value of PGS

MNI coordinates XYZ

0.1

60.3 − 10.6

−4

0.25 0.5 1

56.1 − 11.5 −6.2 56.6 − 10.8 −6.2 58.9 − 10.5 −4.7

Cluster size (mm 2)

Brain region

hemisphere

222.46

STG

right

232.35 268.26 266.94

STG STG STG

right right right

Note: PGS, polygenic score; SWB, subjective well-being; CT, cortical thickness; STG, superior temporal gyrus; cluster size refers to the surface area of cluster; X, Y and Z mean the MNI region X, Y and Z plane, respectively.

differences at all threshold (0.0001: t = 0.657, p = 0.511; 0.001: t = − 0.077, p = 0.938; 0.01: t = 0.495, p = 0.621; 0.05: t =1.017, p = 0.309; 0.1: t = 1.531, p = 0.126; 0.25: t =1.645, p = 0.101; 0.5: t = 1.455, p = 0.146; 1: t = 1.325, p = 0.186).

Relationship between the PGS of SWB and CT The associations between the PGSs of SWB and wholebrain cortical thickness were investigated at eight different thresholds. We found that there were some differences among the eight thresholds. Specifically, at the lower thresholds (0.0001, 0.001, 0.01 and 0.05), there was no significant association between the PGS and CT, but at relatively higher thresholds (0.1, 0.25, 0.5 and 1), all the PGSs

were significantly and positively correlated with the thickness of the right superior temporal gyrus (STG) (Fig. 1A, Table 2). Additionally, there was no significant negative relationship between the PGS and CT. The correlations between the mean CT values of significant brain regions and the Z-valued PGSs were extremely significant (all p < 0.001), see Fig. 1B for more information.

Relationship between the PGS of SWB and the GMV Here, we analyzed the correlations between the PGSs and whole-brain GMV across eight thresholds. The results showed that when the thresholds were less than 0.5, there was no significant association between regional GMV and the PGS of SWB. On the contrary, at 0.5 or 1 threshold, a significant positive correlation between the PGS and GMV in the right insula was found (Fig. 2A, Table 3). The correlations between the mean GMV values of significant brain regions and Z-valued PGSs are shown in Fig. 2B. Additionally, there was no negative significant relationship between the two.

Relationship between behavior, PGS and brain structure In this study, there was a significant correlation between the two behavioral scores of 309 subjects (r = 0.496, p < 0.001). Moreover, there were significant correlations between the Index of WellBeing and the PGSs at some thresholds (0.05: r = 0.126, p = 0.021; 0.1: r = 0.136, p =0.012; 0.25: r = 0.128, p = 0.018; 0.5: r = 0.124, p = 0.023; 1: r = 0.115, p = 0.034), and the association between the PGS and the OHQ was marginal significant at the 0.5 threshold (r = 0.091, p = 0.053). However, we did not find a significant association between the behavioral scores and the mean CT in right STG or the mean GMV in right insula (all p > 0.05).

DISCUSSION Fig. 2. The relation between the PGSs of SWB and GMV. (A) Relations between the GMV and the PGSs at two different thresholds, with gender, age and the ICV as covariates. The GMV of red regions showed significant positive correlations (p < 0.05) with the PGSs of SWB without correction, but the brightest region of each brain remained significant after a Monte Carlo simulation with a vertex-wise threshold of p < 0.001 and a cluster-wise threshold of p < 0.05. (B) Scatter plots of the correlations between the significant regions and the PGSs. R values are shown with gender, age and the ICV as covariates. The two gray lines show the 95% confidence intervals of the best-fit lines.Note: PGS0.5 means the PGS of SWB at the threshold of 0.5, PGS0.5*GMV means the relationship between PGS0.5 and GMV, and so on.

In the present study, we calculated the PGSs of SWB and explored their association with the CT or GMV. We found that the higher the PGS of SWB of the individual, the thicker the cortex of the right STG, or the larger

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unhealthy individuals revealed that there was significant positive correlation between the temporal lobe thickness and executive or cognitive functions (Hartberg et al., 2010; p value of MNI Cluster size Brain hemisphere Achiron et al., 2013). Conclusively, STG was involved in emoPGS coordinates (mm 2) region tion recognition, interpersonal interaction, understanding the XZY psychological state of oneself and others, and the perception 0.5 36.1 −27.1 7.2 193.32 insula right of social information, all of which are closely related to indivi1 35.7 −27.2 7.7 224.35 insula right dual's SWB. Hence, our findings may suggest that common Note: PGS, polygenic score; SWB, subjective well-being; GMV, gray matter genetic variations of SWB can affect the thickness in the right volume; cluster size refers to the surface area of cluster; X, Y and Z mean the MNI region X, Y and Z plane, respectively. STG and then increase levels of SWB. In addition, there were significant positive associations between GMV in the right insula and the PGSs of SWB at differGMV of the right insula, and these results were repeatable ent thresholds. The insula plays important roles in several at some different thresholds of PGS. In addition, there were human cognitive and behavioral abilities (Paulus et al., 2003; significant correlations between the Index of Well-Being and Naqvi et al., 2007; Singer et al., 2009; Nguyen et al., 2016; the PGSs, which indicated that the PGS of SWB could Rolls, 2016; Dolcos et al., 2018). Previous studies showed a partly reflect the degree of SWB in real experience. In consignificant association between the right insula and wellclusion, we provided preliminary evidence of the association being. For instance, the GMV of the right insula was positively between genetic basis of SWB and brain structure. correlated with eudaimonic well-being (Lewis et al., 2014). In The results showed that the PGSs of SWB were positively another study, Kong et al. (2016) established that the fALFF associated with CT in the right STG. A previous research of right insula could predict one's social well-being. There were found that attention to facial emotions activated the right also many studies that indirectly explored the relationship superior temporal sulcus (STS) more than attention to the between the two. For example, research on depression face per se, suggesting that the right STS is important for linked it to SWB (Beekman et al., 1997; Wood and Joseph, facial emotion recognition (Jin et al., 2001). In other studies, 2010; Okbay et al., 2016) and depressed individuals the researchers found a significant positive correlation showed a decrease of GMV of the right insula (Hwang et between the fALFF in the STG and well-being (Kong et al., al., 2010; Reiner et al., 2011; Andreas et al., 2012). Some 2015a, 2015b). Moreover, many studies had found that researchers also found the insula was involved in social the activation of STG was related to social cognition or cognition and emotional processing (Uddin et al., 2017; social perception (Pelphrey et al., 2004; Roland et al., Caldiroli et al., 2018). Moreover, Critchley et al. (2004) 2007; Blakemore, 2008; Takahashi et al., 2008; Kreifelts found that the insula was involved in interoceptive awareet al., 2010). In addition, some studies on healthy and ness (perceive the internal body state). Furthermore, other researchers found that the insula was not only associated with interoception, but also with emotional experience, and the former was the core of the latter (Singer et al., 2009; Lamm and Singer, 2010; Zaki et al., 2012). Thus, the results may suggest that the cumulative genetic variations of SWB can influence the GMV of the right insula, and may also indirectly reflect that SWB is related to the perceptions of the internal body. Consistent with previous studies, we found that the PGS at a more stringent threshold had weaker statistical power (Sabuncu et al., 2012; Ripke et al., 2014; Chen et al., 2018). Fig. 3. Associations between CT and the PGSs of SWB at four different thresholds before a Monte Carlo simulation. Hence, we tried to explore Gender, age and the ICV were regressed. Significant brain regions included the right STG (p < 0.01).Note: whether there were signifiPGS0.0001 means the PGS of SWB at the threshold of 0.0001, PGS0.0001*CT means the relationship between PGS0.0001 and CT, and so on. cant associations between Table 3. Information on the brain regions where the PGSs of SWB and GMV were significantly associated.

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morphology of right STG and insula is partly regulated by genetic factors associated with SWB.

AUTHOR CONTRIBUTIONS Li Song 1 and Jie Meng 1 discussed and implemented the research program. Qiang Liu and Tengbin Huo Fig. 4. Associations between GMV and the PGSs of SWB at two different thresholds before a Monte Carlo simulaprovided language help; tion. Gender, age and the ICV were regressed. Significant brain regions included the right insula (p < 0.01).Note: Kangcheng Wang and PGS0.1 means the PGS of SWB at the threshold of 0.1, PGS0.1*GMV means the relationship between PGS0.1 Xingxing Zhu gave technical and GMV, and so on. guidance; Yiman Li, Zhiting Ren and Xiao Wang collected data; Jiang Qiu, prothe PGSs and brain structure at stringent thresholds before vided comprehensive guidance. 1 These authors multiple comparisons. The results showed that there was a contributed equally to this work. positive association between the PGS and the thickness in the right superior temporal gyrus at each threshold ACKNOWLEDGMENTS (0.0001, 0.001, 0.01 or 0.05) (p < 0.01), and positive assoThis work was supported by the National Natural Science Founciations between the GMV in the right insula and the PGSs dation of China (31470981; 31571137; 31500885; 31600878; at the 0.1 and 0.25 threshold were also found (p < 0.01). 31771231), Project of the National Defense Science and TechSee Fig. 3 and Fig. 4. These findings suggest that there nology Innovation Special Zone, Chang Jiang Young Scholar, was a trend of positive association between lower PGSs National Program for Special Support of Eminent Professionals and brain anatomical structure. (National Program for Support of Top-notch Young ProfesOur study found that there is a relationship between the sionals), the Program for the Top-notch Young Professionals PGS of SWB and CT or GMV, but there are several limitaby Chongqing, the Fundamental Research Funds for the Centions that need further consideration. First, although relatral Universities (SWU1609177), Natural Science Foundation tively consistent results at some different PGSs thresholds of Chongqing (cstc2015jcyjA10106), Fok Ying Tung Education were found, other appropriate samples could not be found Foundation (151023), and the Research Program Funds of to verify the results, and it was no certain whether the the Collaborative Innovation Center of Assessment toward results could be replicated in the samples from Europe. Basic Education Quality at Beijing Normal University. Future studies could possibly test these results with different samples. Second, in this study, the GWAS results of European SWB were used as a reference to calculate the DECLARATIONS OF INTEREST PGSs at different thresholds. However, whether the The authors declare no competing financial interests. GWAS results of SWB from Europe could be directly used in Asian studies is worth further considerations. Third, when calculating the PGS of SWB, each gene locus was regarded independent, but it was actually not the case. There could have been possible gene and gene interactions, such as epistatic effects. Complex multivariate models could be used to broadly analyze future studies on this. Fourth, this study did not find an association between the PGS of SWB and the PFC, possibly due to the selected brain indicators and future studies could explore their relationship. Finally, although the behavioral scores of SWB were significantly associated with the PGSs, there was no significant correlation between the behavioral scores and the observed structural brain measures, which remains to be further studied. In summary, it was found that there were repeatable and significant positive correlations between the PGSs of SWB and CT in the right STG or GMV in the right insula at some different thresholds. Our study may provide an initial imaging–genetic explanation for better understanding the complex phenotype of SWB, and suggests that the brain

REFERENCES Achiron A, Chapman J, Tal S, Bercovich E, Gil H, Achiron A. (2013) Superior temporal gyrus thickness correlates with cognitive performance in multiple sclerosis. Brain Structure and Function 218:943950. Andreas B, Wood SJ, Barnaby N, Dennis V, Murat Y, Tsutomu T, Yung AR, Michael B, et al. (2012) Amygdala and insula volumes prior to illness onset in bipolar disorder: a magnetic resonance imaging study. Psychiatry Res 201:34-39. Andrews FM, McKennell AC. (1980) Measures of self-reported wellbeing: their affective, cognitive, and other components. Social Indicators Research 8:127-155. Archontaki D, Lewis GJ, Bates TC. (2013) Genetic influences on psychological well-being: a nationally representative twin study. J Pers 81:221-230. Baetschmann G. (2014) Heterogeneity in the relationship between happiness and age: evidence from the German SocioEconomic Panel. German Economic Review 15:393-410. Bartels M. (2015) Genetics of wellbeing and its components satisfaction with life, happiness, and quality of life: a review and meta-analysis of heritability studies. Behav Genet 45:137-156.

Li Song et al. / Neuroscience 414 (2019) 210–218 Bartels M, Boomsma DI. (2009) Born to be happy? The etiology of subjective well-being. Behav Genet 39:605-615. Beekman AT, Deeg DJ, Braam AW, Smit JH, Tilburg WV. (1997) Consequences of major and minor depression in later life: a study of disability, well-being and service utilization. Psychol Med 27:1397. Blakemore SJ. (2008) Development of the social brain during adolescence. Q J Exp Psychol 61:40-49. Caldiroli A, Buoli M, Haren NEMV, Nijs JD, Altamura AC, Cahn W (2018), The relationship of IQ and emotional processing with insula volume in schizophrenia. Schizophrenia Research: S0920996418303888-. Campbell A. (1976) Subjective measures of well-being. American Psychologist 31:117-124. Chalise HN, Saito T, Takahashi M, Kai I. (2007) Relationship specialization amongst sources and receivers of social support and its correlations with loneliness and subjective well-being: a cross sectional study of Nepalese older adults. Arch Gerontol Geriatr 44:299-314. Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ. (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 20154-7. Chen H, Pine DS, Ernst M, Gorodetsky E, Kasen S, Gordon K, Goldman D, Cohen P. (2013) The MAOA gene predicts happiness in women. Progress in Neuro-psychopharmacology and Biological Psychiatry 40:122-125. Chen Q, Xu T, Yang W, Li Y, Sun J, Wang K, Beaty RE, Zhang Q, et al. (2015) Individual differences in verbal creative thinking are reflected in the precuneus. Neuropsychologia 75:441-449. Chen Q, Ursini G, Romer AL, Knodt AR, Mezeivtch K, Xiao E, Pergola G, Blasi G, et al. (2018) Schizophrenia polygenic risk score predicts mnemonic hippocampal activity. Brain 141. Clark AE, Frijters P, Shields MA. (2008) Relative income, happiness, and utility: an explanation for the Easterlin paradox and other puzzles. Journal of Economic literature 46:95-144. Cox RW. (2012) AFNI: what a long strange trip it's been. Neuroimage 62:743-747. Critchley HD, Stefan W, Pia R, Arne O, Dolan RJ. (2004) Neural systems supporting interoceptive awareness. Nature Neurosci 7:189195. Das S, Forer L, Schönherr S, Sidore C, Locke AE, Kwong A, Vrieze SI, Chew EY, et al. (2016) Next-generation genotype imputation service and methods. Nat Genet 48:1284-1287. De Neve J-E. (2011) Functional polymorphism (5-HTTLPR) in the serotonin transporter gene is associated with subjective well-being: evidence from a US nationally representative sample. J Hum Genet 56:456-459. Diener E. (2000) Subjective well-being. The science of happiness and a proposal for a national index. American Psychologist 55:34-43. Diener E, Diener M, Diener C. (1995) Factors predicting the subjective well-being of nations. J Pers Soc Psychol 69:851-864. Diener E, Oishi S, Lucas RE. (2009) Subjective well-being: The science of happiness and life satisfaction, 2009187-194. Dolcos S, Moore M, Katsumi Y (2018), Neuroscience and well-being. Handbook of well-being. Salt Lake City, UT: DEF Publishers. Fischl B. (2012) FreeSurfer. Neuroimage 62:774-781. Gallagher EN, Vella-Brodrick DA. (2008) Social support and emotional intelligence as predictors of subjective well-being. Pers Individ Differ 44:1551-1561. Gao J, Davis LK, Hart AB, Sanchez-Roige S, Han L, Cacioppo JT, Palmer AA. (2016) Genome-wide association study of loneliness demonstrates a role for common variation. Neuropsychopharmacology 42:811-821. Graham C, Chattopadhyay S. (2013) Gender and well-being around the world. International Journal of Happiness & Development 1:212. Hartberg CB, Lawyer G, Nyman H, Jönsson EG, Haukvik UK, Saetre P, Bjerkan PS, Andreassen OA, et al. (2010) Investigating relationships between cortical thickness and cognitive performance in patients with schizophrenia and healthy adults. Psychiatry Research Neuroimaging 182:123-133. Heller AS, van Reekum CM, Schaefer SM, Lapate RC, Radler BT, Ryff CD, Davidson RJ. (2013) Sustained striatal activity predicts eudaimonic well-being and cortisol output. Psychol Sci 24:2191-2200.

217

Hills P, Argyle M. (2002) The oxford happiness questionnaire: a compact scale for the measurement of psychological well-being. Pers Individ Differ 33:1073-1082. Holmes AJ, Lee PH, Hollinshead MO, Leah B, Roffman JL, Smoller JW, Buckner RL. (2012) Individual differences in amygdala-medial prefrontal anatomy link negative affect, impaired social functioning, and polygenic depression risk. J Neurosci 32:18087-18100. Hwang J-P, Lee T-W, Tsai S-J, Chen T-J, Yang C-H, Lirng J-F, Tsai CF. (2010) Cortical and subcortical abnormalities in late-onset depression with history of suicide attempts investigated with MRI and voxel-based morphometry. J Geriatr Psychiatry Neurol 23:171-184. Inglehart R. (2002) Gender, aging, and subjective well-being. International Journal of Comparative Sociology 43:391-408. James RN, O'Boyle MW. (2012) An fMRI analysis of subjective wellbeing responses: more positive responses engage brain regions similar to positive cognitive reappraisal. Social Science Electronic Publishing, 2012. Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM. (2012) Fsl. Neuroimage 62:782-790. Jin N, Okada T, Sadato N, Fukui K, Yonekura Y. (2001) Attention to emotion modulates fMRI activity in human right superior temporal sulcus. Brain Res Cogn Brain Res 12:225-231. Kaliterna L, Burusic J. (2014) Age and gender differences in well-being in Croatia. Netherlands: Springer, 2014. Kong F., Hu S., Wang X., Song Y., Liu J.. (2015) Neural correlates of the happy life: the amplitude of spontaneous low frequency fluctuations predicts subjective well-being. Neuroimage 107:136-145. Kong F., Liu L., Wang X., Hu S., Song Y., Liu J.. (2015) Different neural pathways linking personality traits and eudaimonic well-being: a resting-state functional magnetic resonance imaging study. Cogn Affect Behav Neurosci 15:299-309. Kong F, Hu S, Song X, Song Y, Liu J. (2015) Extraversion mediates the relationship between structural variations in the dorsolateral prefrontal cortex and social well-being. Neuroimage 105:269-275. Kong F, Song X, Wang X. (2016) Amplitude of low frequency fluctuations during resting state predicts social well-being. Biol Psychol 118:161-168. Kreifelts B, Ethofer TE, Grodd W, Wildgruber D. (2010) Association of trait emotional intelligence and individual fMRI-activation patterns during the perception of social signals from voice and face. Hum Brain Mapp 31:979-991. Lamm C, Singer T. (2010) The role of anterior insular cortex in social emotions. Brain Struct Funct 214:579-591. Lee JJ, Wedow R, Okbay A, Kong E, Maghzian O, Zacher M, Nguyenviet TA, Bowers P, et al. (2018) Gene discovery and polygenic prediction from a genome-wide association study of educational attainment in 1.1 million individuals. Nat Genet :1112-1121. Lewis GJ, Kanai R, Rees G, Bates TC. (2014) Neural correlates of the 'good life': eudaimonic well-being is associated with insular cortex volume. Social Cognitive & Affective Neuroscience 9:615. Liu B, Zhang X, Cui Y, Qin W, Yan T, Li J, Yu C, Jiang T. (2016) Polygenic risk for schizophrenia influences cortical gyrification in 2 independent general populations. Schizophr Bull 43:673-680. Luo Y, Huang X, Yang Z, Li B, Liu J, Wei D. (2014) Regional homogeneity of intrinsic brain activity in happy and unhappy individuals. Plos One 9e85181. Luo Y, Chen X, Qi S, You X, Huang X. (2018) Well-being and anticipation for future positive events: evidences from an fMRI study. Front Psychol 8:2199. Meng J, Hao L, Wei D, Sun J, Li Y, Qiu J, Meng J, Hao L, et al. (2017) BDNF Val66Met polymorphism modulates the effect of loneliness on white matter microstructure in young adults. Biol Psychol 130. Naqvi NH, Rudrauf D, Damasio H, Bechara A. (2007) Damage to the insula disrupts addiction to cigarette smoking. Science 315:531-534. Nguyen VT, Breakspear M, Hu X, Guo CC. (2016) The integration of the internal and external milieu in the insula during dynamic emotional experiences. Neuroimage 124:455-463. Okbay A, Baselmans BM, De Neve JE, Turley P, Nivard MG, Fontana MA, Meddens SF, Linnér RK, et al. (2016) Genetic variants

218

Li Song et al. / Neuroscience 414 (2019) 210–218

associated with subjective well-being, depressive symptoms, and neuroticism identified through genome-wide analyses. Nat Genet 48:624. Patterson N, Price AL, Reich D. (2006) Population structure and eigenanalysis. PLoS Genet 2:e190. Paulus MP, Rogalsky C, Simmons A, Feinstein JS, Stein MB. (2003) Increased activation in the right insula during risk-taking decision making is related to harm avoidance and neuroticism. Neuroimage 19:14391448. Pelphrey KA, Morris JP, Gregory MC. (2004) Grasping the intentions of others: the perceived intentionality of an action influences activity in the superior temporal sulcus during social perception. Cognitive Neuroscience Journal of 16:1706-1716. Peng YU, Shu-Hua SU. (2005) The relationships between college students' attributional style, self-efficacy and subjective well-being. Chin J Clin Psych :528. Pinquart M, Sörensen S. (2001) Gender differences in self-concept and psychological well-being in old age: a meta-analysis. J Gerontol B Psychol Sci Soc Sci 56:195-213. Plagnol AC, Easterlin RA. (2008) Aspirations, attainments, and satisfaction: life cycle differences between American women and men. Journal of Happiness Studies 9:601-619. Power RA, Steinberg S, Bjornsdottir G, Rietveld CA, Abdellaoui A, Nivard MM, Johannesson M, Galesloot TE, et al. (2015) Polygenic risk scores for schizophrenia and bipolar disorder predict creativity. Nat Neurosci 18:953-955. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, David R. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38:904-909. Purcell SM, Neale B, Todd-Brown K, Thomas L, Ferreira MAR, Bender D, Maller J, Sklar P, et al. (2007) PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81:559-575. Purcell SM, Wray NR, Stone JL, Visscher PM, O'Donovan MC, Sullivan PF, Sklar P, Ruderfer DM, et al. (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460:748-752. Reekum CMV, Urry HL, Johnstone T, Thurow ME, Frye CJ, Jackson CA, Schaefer HS, Alexander AL, et al. (2007) Individual differences in amygdala and ventromedial prefrontal cortex activity are associated with evaluation speed and psychological well-being. J Cogn Neurosci 19:237. Reiner S, Douglas J, Benson M, Poornima K, David C, Maarten M, Keith M. (2011) The insular cortex and the neuroanatomy of major depression. J Affect Disord 133:120-127. Ripke S, Neale BM, Corvin A, Walters JTR, Farh K-H, Holmans PA, Lee P, Bulik-Sullivan B, et al. (2014) Biological insights from 108 schizophrenia-associated genetic loci. Nature 511:421-427. Roland Z, Jorge M, Frank K, Huey ED, Griselda G, Jordan G. (2007) Social concepts are represented in the superior anterior temporal cortex. Proc Natl Acad Sci U S A 104:6430-6435. Rolls ET. (2016) Functions of the anterior insula in taste, autonomic, and related functions. Brain Cogn 110:4-19. Ryan RM, Deci EL. (2001) On happiness and human potentials: a review of research on hedonic and eudaimonic well-being. Annu Rev Psychol 52:141-166. Sabuncu MR, Buckner RL, Smoller JW, Phil Hyoun L, Bruce F, Sperling RA. (2012) The association between a polygenic Alzheimer score and cortical thickness in clinically normal subjects. Cereb Cortex 22:2653-2661. Sánchezálvarez N, Extremera N, Fernándezberrocal P. (2016) The relation between emotional intelligence and subjective well-being: a metaanalytic investigation. Journal of Positive Psychology 11:276-285. Singer T, Critchley HD, Preuschoff K. (2009) A common role of insula in feelings, empathy and uncertainty. Trends Cogn Sci 13:334-340.

Strobel M, Tumasjan A, Spörrle M. (2011) Be yourself, believe in yourself, and be happy: self-efficacy as a mediator between personality factors and subjective well-being. Scand J Psychol 52:43-48. Suardi A, Sotgiu I, Costa T, Cauda F, Rusconi M. (2016) The neural correlates of happiness: a review of PET and fMRI studies using autobiographical recall methods. Cogn Affect Behav Neurosci 16:383392. Takahashi H, Matsuura M, Koeda M, Yahata N, Suhara T, Kato M, Okubo Y. (2008) Brain activations during judgments of positive self-conscious emotion and positive basic emotion: pride and joy. Cereb Cortex 18:898-903. Tielbeek JJ, Johansson A, Tjc P, Rautiainen MR, Jansen P, Taylor M, Tong X, Lu Q, et al. (2017) Genome-wide association studies of a broad spectrum of antisocial behavior. JAMA Psychiat 74:1242. Tu Y, Yang Z. (2016) Self-control as mediator and moderator of the relationship between social support and subjective well-being among the Chinese elderly. Social Indicators Research 126:813-828. Turley P, Walters RK, Maghzian O, Okbay A, Lee JJ, Fontana MA, Nguyenviet TA, Wedow R, et al. (2018) Multi-trait analysis of genome-wide association summary statistics using MTAG. Nat Genet 21:16. Uddin L.Q., Nomi J.S., Hébert-Seropian B., Ghaziri J., Boucher O.. (2017) Structure and function of the human insula. J Clin Neurophysiol 34:300-306. Van't ED, Den BA, Bml B, Brouwer RM, Dolan CV, Hulshoff Pol HE, Ejc DG, Bartels M. (2017) Associations between subjective well-being and subcortical brain volumes. Sci Rep 7:6957. Vijayakumar N, Whittle S, Yücel M, Dennison M, Simmons J, Allen NB. (2014) Thinning of the lateral prefrontal cortex during adolescence predicts emotion regulation in females. Social Cognitive & Affective Neuroscience 9:1845. Winkler AM, Kochunov PJ, Almasy L, Zilles K, Fox PT, Duggirala R, Glahn DC. (2010) Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage 53:1135-1146. Wood AM, Joseph S. (2010) The absence of positive psychological (eudemonic) well-being as a risk factor for depression: a ten year cohort study. J Affect Disord 122:213-217. Xu T, Yang Z, Jiang L, Xing XX, Zuo XN. (2015) A connectome computation system for discovery science of brain. Science Bulletin 60:86-95. Yang X, Yang Y, Xue M, Fang P, Shen G, Zhang K, Gao X, Yu R, et al. (2017) Independent self-construal mediates the association between CYP19A1 gene variant and subjective well-being. Consciousness & Cognition 55:205-213. Yuan J, Zhang J, Zhou X, Yang J, Meng X, Zhang Q, Hong L. (2012) Neural mechanisms underlying the higher levels of subjective wellbeing in extraverts: pleasant bias and unpleasant resistance. Cogn Affect Behav Neurosci 12:175-192. Zaki J, Davis JI, Ochsner KN. (2012) Overlapping activity in anterior insula during interoception and emotional experience. Neuroimage 62:493-499. Glossary

GWAS: genome-wide association study, links trait of interest with genetic variations at genomic loci to identify complex trait susceptibility loci. SNP: single nucleotide polymorphism, mainly refers to the DNA sequence polymorphism caused by the variation of single nucleotide at the level of genome. It is one of the most common types of genetic variation in humans. PGS: polygenic score, aggregates the small effect of thousands of common DNA variations from GWAS, and it has the potential to make genetic prediction for individual corresponding trait.

(Received 4 March 2019, Accepted 27 May 2019) (Available online 4 June 2019)