1 INTRODUCTION

Schizophrenia and bipolar disorder are highly heritable disorders with a shared genetic architecture (Anttila et al., 2018; Lee et al., 2013; Lichtenstein et al., 2009). Both patient groups are characterized by overlapping patterns of structural brain abnormalities (Arnone et al., 2009; Ellison-Wright & Bullmore, 2010; Haijma et al., 2013; Hibar et al., 2016; Hibar et al., 2018; Ivleva et al., 2017; McDonald et al., 2004; Okada et al., 2016; van Erp et al., 2016, 2018). In contrast, our recent ENIGMA–Relatives meta-analysis showed that their family members—who share the risk for the disorder but generally are not confounded by medication use or other illness related factors—show divergent patterns of global brain measures (de Zwarte, Brouwer, Agartz, et al., 2019). That study found that first-degree relatives of patients diagnosed with bipolar disorder (BD-FDRs) had a larger intracranial volume (ICV) which was not present in first-degree relatives of patients diagnosed with schizophrenia (SZ-FDRs). When we adjusted for ICV, no differences were found between BD-FDRs and controls but SZ-FDRs still showed significantly smaller brain volumes, diminished cortical thickness and larger ventricle volume compared to controls. These findings suggest that individuals at familial risk for either bipolar disorder or schizophrenia may show disease-specific deviations during early brain development.

Differential neurodevelopmental trajectories in schizophrenia and bipolar disorder have also been linked to intelligence quotient (IQ) development and school performance (Parellada, Gomez-Vallejo, Burdeus, & Arango, 2017). Schizophrenia has been associated with poorer cognitive performance, as well as decreases in cognitive performance over time, years before onset (Agnew-Blais & Seidman, 2013; Dickson, Laurens, Cullen, & Hodgins, 2012; Hochberger et al., 2018; Kendler, Ohlsson, Sundquist, & Sundquist, 2015; Khandaker, Barnett, White, & Jones, 2011; Reichenberg et al., 2005; Woodberry, Giuliano, & Seidman, 2008), while premorbid IQ or educational attainment are often not affected or are even higher in individuals who later develop bipolar disorder (MacCabe et al., 2010; Smith et al., 2015; Tiihonen et al., 2005; Zammit et al., 2004).

Both IQ and educational attainment are highly heritable (Devlin, Daniels, & Roeder, 1997; Heath et al., 1985; Tambs, Sundet, Magnus, & Berg, 1989). Consequently, similar patterns of cognitive performance and educational attainment are often found among relatives. Indeed, cognitive alterations have been reported in SZ-FDRs compared to controls (Hughes et al., 2005; Kremen, Faraone, Seidman, Pepple, & Tsuang, 1998; McIntosh, Harrison, Forrester, Lawrie, & Johnstone, 2005; Niendam et al., 2003; Sitskoorn, Aleman, Ebisch, Appels, & Kahn, 2004; Van Haren, Van Dam, & Stellato, 2019; Vreeker et al., 2016) and in BD-FDRs compared to controls (Vonk et al., 2012; Vreeker et al., 2016). Vreeker et al. (2016) showed, in a direct comparison, a discrepancy between IQ and educational attainment in SZ-FDRs and BD-FDRs: both groups showed lower IQ but similar educational attainment compared to controls. These findings suggest that, despite the high genetic and phenotypic overlap between intelligence and educational attainment in the general population (Sniekers et al., 2017; Strenze, 2007), it is important to differentiate between these two measures when investigating individuals at familial risk for mental illness.

Intelligence has consistently been associated with brain structure in the general population (McDaniel, 2005). Our recent schizophrenia family studies reported that IQ is intertwined with most of the brain abnormalities in SZ-FDRs (de Zwarte, Brouwer, Tsouli, et al., 2019; van Haren et al., 2020). Altered brain structure and cognitive deficits observed in schizophrenia patients could be a direct consequence of the association observed in the general population or alternatively, both could be caused by the disease through independent mechanisms. IQ and brain structure are both considered indirect measures for (early) neurodevelopment. Indeed, both brain structure and cognitive deficits in schizophrenia relatives are apparent in children and adolescents at high familial risk (van Haren et al., 2020), suggesting that individuals at familial risk for schizophrenia show altered neurodevelopment already early in life. This would suggest that genetic risk for the disease influences both cognition and the brain and we cannot study one without the other. However, in BD-FDRs, it remains unknown how IQ and risk for bipolar disorder interact with the brain. In particular, the relationship between IQ and the familial predisposition for a larger ICV in BD-FDRs is unclear. Hence, it is of interest to see how brain anomalies and intelligence differences are related to each other in those at familial risk for schizophrenia and bipolar disorder.

Here, through the Enhancing Neuro Imaging Genetics Through Meta Analysis (ENIGMA)-Relatives Working Group, we performed meta-analyses of magnetic resonance imaging data sets consisting of SZ-FDRs and/or BD-FDRs, probands, and matched control participants. There were three main aims. First, we extended our findings of group differences in global brain measures between relatives and controls (and patients) for both disorders (de Zwarte, Brouwer, Agartz, et al., 2019) by adding local cortical measures. This allowed us to investigate whether the findings were limited to specific (functional) brain regions, or can be attributed to a more global mechanism. Previous ENIGMA meta-analyses have shown that patients with schizophrenia have widespread attenuation of cortical thickness and surface area (with largest effects in frontal and temporal lobe regions), with evidence for regional specificity only in the thickness findings (van Erp et al., 2018). In contrast, patients with bipolar disorder have shown thinner cortex in frontal, temporal and parietal regions, but no differences in surface area, compared to controls (Hibar et al., 2018). Based on the patient findings and our previous ENIGMA-Relatives findings for global brain measures—showing globally thinner cortex in SZ-FDRs and larger surface area in BD-FDRs—we expected to find subtle regional differences in these measures in the relatives. In particular, we predicted locally thinner cortex in SZ-FDRs with a similar pattern to previous observations in patients but with smaller effect sizes (van Erp et al., 2018). Based on the larger ICV and global surface area reported in our previous study, locally larger surface area in BD-FDRs was expected in contrast to previous bipolar patient findings (Hibar et al., 2018). Second, in cohorts that had information on current IQ and/or educational attainment (the latter is defined as years of education completed), we meta-analyzed the group effects of IQ and educational attainment between relatives and controls (and patients) for both disorders. We hypothesized that both SZ-FDRs and BD-FDRs would have, on average, lower current IQ than controls. Educational attainment findings in relatives have been inconsistent, with findings of both lower educational attainment and no detectable differences between relatives and controls; therefore, we expected subtle but significant differences between both SZ-FDRs and BD-FDRs and controls. Third, we investigated the influence of IQ and educational attainment on global and local brain differences between relatives and controls. We hypothesized that IQ will account for most of the brain abnormalities found in SZ-FDRs, while a lower IQ most likely would not explain our previously reported larger ICV in BD-FDRs because of the well- established positive relationship between overall head size and IQ (McDaniel, 2005). The moderating effect of educational attainment on brain abnormalities is expected to be less pronounced than that of IQ, as we are only expecting modest group differences in educational attainment between relatives and controls.

2 MATERIALS AND METHODS 2.1 Study samples

This study included 5,795 participants from 36 family cohorts (age range 6–72 years). In total, 1,103 SZ-FDRs (42 monozygotic co-twins, 50 dizygotic co-twins, 171 offspring, 728 siblings, 112 parents), 867 BD-FDRs (32 monozygotic co-twins, 33 dizygotic co-twins, 453 offspring, 331 siblings, 18 parents), 942 patients with schizophrenia, 693 patients with bipolar disorder and 2,190 controls were included (Tables 1 and 2). All family cohorts included their own control participants. Controls did not have a family history of schizophrenia or bipolar disorder. SZ-FDRs or BD-FDRs were defined by having a first-degree family member with schizophrenia or bipolar disorder, respectively, and not having experienced (hypo)mania and/or psychosis themselves. Demographic characteristics for each cohort and their inclusion criteria are summarized in Tables 1 and 2 and Table S1. The cohorts in the current meta-analysis overlap largely, but not completely with those in our previous meta-analysis (de Zwarte, Brouwer, Agartz, et al., 2019). All study centers obtained approval from their respective ethics committee for research, following the Declaration of Helsinki. Informed consent was obtained from all participants and/or parents, in the case of minors.

TABLE 1. Sample demographics bipolar disorder family cohorts Total IQ scores Educational attainment Controls Patients Relatives Controls Patients Relatives Controls Patients Relatives Sample N M/F Age N M/F Age N M/F Age N IQ N IQ N IQ N EA N EA N EA BPO-FLB 7 3/4 12.9 (1.3) 9 5/4 13.3 (2.6) 22 10/12 10.0 (3.5) 7 91.0 (10.2) 5 91.2 (16.1) 7 95.4 (17.3) — — — Cardiff 79 28/51 39.8 (8.7) 120 42/78 41.9 (8.1) 33 13/20 45.9 (6.9) — — — — — — CliNG-BDa 19 6/13 30.9 (9.6) — 19 6/13 31.9 (5.0) — — — 12 14.9 (3.3) — 10 15.2 (3.2) DEU 27 11/16 32.9 (8.8) 27 10/17 36.3 (9.5) 23 11/12 31.3 (8.9) — — — 21 13.1 (4.1) 24 12.9 (2.9) 14 11.6 (3.1) EGEU 33 13/20 33.6 (7.8) 27 16/11 36.7 (7.8) 27 10/17 34.5 (9.5) — — — 28 11.6 (3.8) 26 10.8 (4.2) 23 10.8 (4.4) ENBD-UT 36 13/23 34.8 (11.7) 72 23/49 36.9 (12.4) 52 10/42 44.3 (13.6) 27 101.0 (14.5) 40 97.0 (12.0) 19 99.2 (14.4) 26 15.2 (3.0) 55 14.7 (2.3) 46 15.1 (2.3) FIDMAG-Clinic 61 12/49 41.1 (10.1) 18 3/15 42.6 (8.8) 18 5/13 45.1 (10.0) 61 112.9 (13.6) 14 101.9 (13.1) 16 105.8 (16.7) — — — Geneva 19 10/9 20.1 (2.7) — 18 9/9 19.4 (3.1) — — — — — — IDIBAPSa 53 21/32 12.3 (3.6) — 61 31/30 12.4 (3.4) 53 106.1 (12.4) — 61 107.0 (13.0) — — — IoP-BD 39 9/30 35.4 (11.2) 34 15/19 40.6 (13.1) 17 4/13 43.1 (14.6) — — — 31 15.2 (2.6) 26 15.4 (3.2) 14 16.4 (2.6) MFS-BDa 54 25/29 40.2 (15.3) 38 15/23 41.0 (11.7) 41 17/24 49.3 (9.6) 39 110.8 (16.1) 31 97.4 (11.7) 34 100.0 (10.3) 35 14.1 (3.9) 35 13.9 (3.3) 31 14.6 (4.0) MooDS-BDa 63 25/38 30.3 (9.5) — 63 25/38 30.4 (9.4) 62 99.4 (5.5) — 62 101.5 (5.8) 33 15.4 (2.4) — 34 17.2 (2.8) MSSM 52 25/27 35.2 (13.0) 41 21/20 44.3 (11.9) 50 26/24 33.8 (8.3) — — — — — — Olin 68 25/43 32.2 (11.7) 108 34/74 34.5 (12.3) 78 30/48 32.0 (13.0) 54 107.0 (15.0) 95 102.9 (15.6) 68 105.6 (15.1) 40 15.2 (2.4) 74 14.6 (2.2) 40 14.6 (2.2) ORBIS-I 32 12/20 20.7 (3.3) 6 0/6 22.9 (4.0) 39 13/26 19.8 (3.2) — — — — — — ORBIS-II 18 7/11 23.0 (3.5) 8 3/5 24.0 (5.0) 26 10/16 19.9 (4.0) — — — — — — PENS-BDa 16 6/10 45.9 (10.1) 20 14/6 46.9 (10.4) 9 5/4 40.4 (6.3) 16 115.7 (13.8) 20 103.7 (15.8) 9 101.3 (18.0) 16 16.0 (1.3) 19 14.8 (2.7) 9 15.0 (1.6) PHCP-BDa 38 21/17 38.4 (13.7) 29 7/22 32.2 (11.6) 7 2/5 51.0 (6.1) 38 106.3 (11.8) 29 101.8 (8.8) 7 100.6 (9.1) 29 16.0 (2.5) 18 14.8 (1.8) 7 15.4 (1.5) STAR-BDa 83 39/44 49.0 (10.4) 25 7/18 45.8 (10.1) 21 6/15 47.9 (11.3) — — — 81 11.9 (2.9) 25 12.9 (3.6) 21 11.5 (2.5) SydneyBipolarGroup 117 54/63 22.2 (3.9) 59 17/42 25.1 (3.6) 150 65/85 19.9 (5.4) 116 117.6 (10.3) 57 116.2 (12.3) 147 114.5 (10.6) 24 17.1 (3.2) 32 16.4 (2.3) 30 15.9 (2.2) UMCU-BD twinsa 110 40/70 39.3 (9.2) 52 13/39 39.6 (9.7) 27 9/18 41.7 (9.3) 48 98.0 (13.5) 22 92.4 (13.2) 14 95.4 (14.1) 108 13.4 (2.7) 47 12.7 (2.6) 26 12.1 (2.6) UMCU-DBSOS