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Distinctions between major depressive disorder (MDD) and perinatal depression (PND) reflect varying views of PND, from a unique etiological subtype of MDD to an MDD episode that happens to coincide with childbirth. This case-control study investigated genetic differences between PND and MDD outside the perinatal period (non-perinatal depression or NPD).We conducted a genome-wide association study using PND cases (Edinburgh Postnatal Depression Scale score ≥ 13) from the Australian Genetics of Depression Study 2018 data (n = 3804) and screened controls (n = 6134). Results of gene-set enrichment analysis were compared with those of women with non-PND. For six psychiatric disorders/traits, genetic correlations with PND were evaluated, and logistic regression analysis reported polygenic score (PGS) association with both PND and NPD.Genes differentially expressed in ovarian tissue were significantly enriched (stdBeta = 0.07, p = 3.3e-04), but were not found to be associated with NPD. The genetic correlation between PND and MDD was 0.93 (SE = 0.07; p = 3.5e-38). Compared with controls, PGS for MDD are higher for PND cases (odds ratio [OR] = 1.8, confidence interval [CI] = [1.7-1.8], p = 9.5e-140) than for NPD cases (OR = 1.6, CI = [1.5-1.7], p = 1.2e-49). Highest risk is for those reporting both antenatal and postnatal depression, irrespective of prior MDD history.PND has a high genetic overlap with MDD, but points of distinction focus on differential expression in ovarian tissue and higher MDD PGS, particularly for women experiencing both antenatal and postpartum PND.

Special Issue: The ENIGMA Consortium: the first 10 years., Delineating the association of age and cortical thickness in healthy individuals is critical given the association of cortical thickness with cognition and behavior. Previous research has shown that robust estimates of the association between age and brain morphometry require large-scale studies. In response, we used cross-sectional data from 17,075 individuals aged 3–90 years from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to infer age-related changes in cortical thickness. We used fractional polynomial (FP) regression to quantify the association between age and cortical thickness, and we computed normalized growth centiles using the parametric Lambda, Mu, and Sigma method. Interindividual variability was estimated using meta-analysis and one-way analysis of variance. For most regions, their highest cortical thickness value was observed in childhood. Age and cortical thickness showed a negative association; the slope was steeper up to the third decade of life and more gradual thereafter; notable exceptions to this general pattern were entorhinal, temporopolar, and anterior cingulate cortices. Interindividual variability was largest in temporal and frontal regions across the lifespan. Age and its FP combinations explained up to 59% variance in cortical thickness. These results may form the basis of further investigation on normative deviation in cortical thickness and its significance for behavioral and cognitive outcomes., European Community's Seventh Framework Programme, Grant/Award Numbers: 278948, 602450, 603016, 602805; US National Institute of Child Health and Human Development, Grant/Award Numbers: RO1HD050735, 1009064, 496682; QIMR Berghofer Medical Research Institute and the Centre for Advanced Imaging, University of Queensland; ICTSI NIH/NCRR, Grant/Award Number: RR025761; European Community's Horizon 2020 Programme, Grant/Award Numbers: 667302, 643051; Vici Innovation Program, Grant/Award Numbers: #91619115, 016-130-669; NWO Brain & Cognition Excellence Program, Grant/Award Number: 433-09-229; Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL); Spinozapremie, Grant/Award Number: NWO-56-464-14192; Biobanking and Biomolecular Resources Research Infrastructure, Grant/Award Numbers: 184.033.111, 184.021.007; Netherlands Organization for Health Research and Development (ZonMW), Grant/Award Numbers: 480-15-001/674, 024.001.003, 911-09-032, 056-32-010, 481-08-011, 016-115-035, 31160008, 400-07-080, 400-05-717, 451-04-034, 463-06-001, 480-04-004, 904-61-193, 912-10-020, 985-10-002, 904-61-090; NIMH, Grant/Award Number: R01 MH090553; Geestkracht programme of the Dutch Health Research Council, Grant/Award Number: 10-000-1001; FP7 Ideas: European Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, Grant/Award Numbers: NWO/SPI 56-464-14192, NWO-MagW 480-04-004, 433-09-220, NWO 51.02.062, NWO 51.02.061; National Center for Advancing Translational Sciences, National Institutes of Health, Grant/Award Number: UL1 TR000153; National Center for Research Resources; National Center for Research Resources at the National Institutes of Health, Grant/Award Numbers: NIH 1U24 RR025736-01, NIH 1U24 RR021992; NIH Institutes contributing to the Big Data to Knowledge; U.S. National Institutes of Health, Grant/Award Numbers: R01 CA101318, P30 AG10133, R01 AG19771; Medical Research Council, Grant/Award Numbers: U54EB020403, G0500092; National Institute of Mental Health, Grant/Award Numbers: R01MH117014, R01MH042191; Fundación Instituto de Investigación Marqués de Valdecilla, Grant/Award Numbers: API07/011, NCT02534363, NCT0235832; Instituto de Salud Carlos III, Grant/Award Numbers: PI14/00918, PI14/00639, PI060507, PI050427, PI020499; Swedish Research Council, Grant/Award Numbers: 523-2014-3467, 2017-00949, 521-2014-3487; South-Eastern Norway Health Authority; the Research Council of Norway, Grant/Award Number: 223273; South Eastern Norway Regional Health Authority, Grant/Award Numbers: 2017-112, 2019107; Icahn School of Medicine at Mount Sinai; Seventh Framework Programme (FP7/2007-2013), Grant/Award Number: 602450; National Institutes of Health, Grant/Award Numbers: R01 MH116147, R01 MH113619, R01 MH104284; South London and Maudsley NHS Foundation Trust; the National Institute for Health Research (NIHR)

Comparing twins from same- and opposite-sex pairs can provide information on potential sex differences in a variety of outcomes, including socioeconomic-related outcomes such as educational attainment. It has been suggested that this design can be applied to examine the putative role of intrauterine exposure to testosterone for educational attainment, but the evidence is still disputed. Thus, we established an international database of twin data from 11 countries with 88,290 individual dizygotic twins born over 100 years and tested for differences between twins from same- and opposite-sex dizygotic pairs in educational attainment. Effect sizes with 95% confidence intervals (CI) were estimated by linear regression models after adjusting for birth year and twin study cohort. In contrast to the hypothesis, no difference was found in women (β = −0.05 educational years, 95% CI −0.11, 0.02). However, men with a same-sex co-twin were slightly more educated than men having an opposite-sex co-twin (β = 0.14 educational years, 95% CI 0.07, 0.21). No consistent differences in effect sizes were found between individual twin study cohorts representing Europe, the USA, and Australia or over the cohorts born during the 20th century, during which period the sex differences in education reversed favoring women in the latest birth cohorts. Further, no interaction was found with maternal or paternal education. Our results contradict the hypothesis that there would be differences in the intrauterine testosterone levels between same-sex and opposite-sex female twins affecting education. Our findings in men may point to social dynamics within same-sex twin pairs that may benefit men in their educational careers. CC BY 4.0Correspondence Address: Silventoinen, K.; University of Helsinki, P.O. Box 18, Finland; email: karri.silventoinen@helsinki.fi© 2021 The Authors

BackgroundMajor depressive disorder (MDD) is associated with an increased risk of brain atrophy, aging-related diseases, and mortality. We examined potential advanced brain aging in MDD patients, and whether this process is associated with clinical characteristics in a large multi-center international dataset.MethodsWe performed a mega-analysis by pooling brain measures derived from T1-weighted MRI scans from 29 samples worldwide. Normative brain aging was estimated by predicting chronological age (10-75 years) from 7 subcortical volumes, 34 cortical thickness and 34 surface area, lateral ventricles and total intracranial volume measures separately in 1,147 male and 1,386 female controls from the ENIGMA MDD working group. The learned model parameters were applied to 1,089 male controls and 1,167 depressed males, and 1,326 female controls and 2,044 depressed females to obtain independent unbiased brain-based age predictions. The difference between predicted “brain age” and chronological age was calculated to indicate brain predicted age difference (brain-PAD).FindingsOn average, MDD patients showed a higher brain-PAD of +0.90 (SE 0.21) years (Cohen’s d=0.12, 95% CI 0.06-0.17) compared to controls. Relative to controls, first-episode and currently depressed patients showed higher brain-PAD (+1.2 [0.3] years), and the largest effect was observed in those with late-onset depression (+1.7 [0.7] years). In addition, higher brain-PAD was associated with higher self-reported depressive symptomatology (b=0.05, p=0.004).InterpretationThis highly powered collaborative effort showed subtle patterns of abnormal structural brain aging in MDD. Substantial within-group variance and overlap between groups were observed. Longitudinal studies of MDD and somatic health outcomes are needed to further assess the predictive value of these brain-PAD estimates.FundingThis work was supported, in part, by NIH grants U54 EB020403 and R01 MH116147.

The ‘16Up’ study conducted at the QIMR Berghofer Medical Research Institute from January 2014 to December 2018 aimed to examine the physical and mental health of young Australian twins aged 16−18 years (N = 876; 371 twin pairs and 18 triplet sets). Measurements included online questionnaires covering physical and mental health as well as information and communication technology (ICT) use, actigraphy, sleep diaries and hair samples to determine cortisol concentrations. Study participants generally rated themselves as being in good physical (79%) and mental (73%) health and reported lower rates of psychological distress and exposure to alcohol, tobacco products or other substances than previously reported for this age group in the Australian population. Daily or near-daily online activity was almost universal among study participants, with no differences noted between males and females in terms of frequency or duration of internet access. Patterns of ICT use in this sample indicated that the respondents were more likely to use online information sources for researching physical health issues than for mental health or substance use issues, and that they generally reported partial levels of satisfaction with the mental health information they found online. This suggests that internet-based mental health resources can be readily accessed by adolescent Australians, and their computer literacy augurs well for future access to online health resources. In combination with other data collected as part of the ongoing Brisbane Longitudinal Twin Study, the 16Up project provides a valuable resource for the longitudinal investigation of genetic and environmental contributions to phenotypic variation in a variety of human traits.

ENIGMA-CNV Working Group: van der Meer, Dennis; Sonderby, Ida E; Kaufmann, Tobias; Walters, G Bragi; Abdellaoui, Abdel; Ames, David; Amunts, Katrin; Andersson, Micael; Armstrong, Nicola J; Bernard, Manon; Blackburn, Nicholas B; Blangero, John; Boomsma, Dorret I; Brodaty, Henry; Brouwer, Rachel M; Bulow, Robin; Cahn, Wiepke; Calhoun, Vince D; Caspers, Svenja; Cavalleri, Gianpiero L; Ching, Christopher R K; Cichon, Sven; Ciufolini, Simone; Corvin, Aiden; Crespo-Facorro, Benedicto; Curran, Joanne E; Dalvie, Shareefa; Dazzan, Paola; de Geus, Eco J C; de Zubicaray, Greig I; de Zwarte, Sonja M C; Delanty, Norman; den Braber, Anouk; Desrivieres, Sylvane; Di Forti, Marta; Doherty, Joanne L; Donohoe, Gary; Ehrlich, Stefan; Eising, Else; Espeseth, Thomas; Fisher, Simon E; Fladby, Tormod; Frei, Oleksandr; Frouin, Vincent; Fukunaga, Masaki; Gareau, Thomas; Glahn, David C; Grabe, Hans J; Groenewold, Nynke A; Gustafsson, Omar; Haavik, Jan; Haberg, Asta K; Hashimoto, Ryota; Hehir-Kwa, Jayne Y; Hibar, Derrek P; Hillegers, Manon H J; Hoffmann, Per; Holleran, Laurena; Hottenga, Jouke-Jan; Hulshoff Pol, Hilleke E; Ikeda, Masashi; Jacquemont, Sebastien; Jahanshad, Neda; Jockwitz, Christiane; Johansson, Stefan; Jonsson, Erik G; Kikuchi, Masataka; Knowles, Emma E M; Kwok, John B; Le Hellard, Stephanie; Linden, David E J; Liu, Jingyu; Lundervold, Arvid; Lundervold, Astri J; Martin, Nicholas G; Mather, Karen A; Mathias, Samuel R; McMahon, Katie L; McRae, Allan F; Medland, Sarah E; Moberget, Torgeir; Moreau, Clara; Morris, Derek W; Muhleisen, Thomas W; Murray, Robin M; Nordvik, Jan E; Nyberg, Lars; Olde Loohuis, Loes M; Ophoff, Roel A; Owen, Michael J; Paus, Tomas; Pausova, Zdenka; Peralta, Juan M; Pike, Bruce; Prieto, Carlos; Quinlan, Erin Burke; Reinbold, Celine S; Reis Marques, Tiago; Rucker, James J H; Sachdev, Perminder S; Sando, Sigrid B; Schofield, Peter R; Schork, Andrew J; Schumann, Gunter; Shin, Jean; Shumskaya, Elena; Silva, Ana I; Sisodiya, Sanjay M; Steen, Vidar M; Stein, Dan J; Strike, Lachlan T; Tamnes, Christian K; Teumer, Alexander; Thalamuthu, Anbupalam; Tordesillas-Gutierrez, Diana; Uhlmann, Anne; Ulfarsson, Magnus O; van 't Ent, Dennis; van den Bree, Marianne B M; Vassos, Evangelos; Wen, Wei; Wittfeld, Katharina; Wright, Margaret J; Zayats, Tetyana; Dale, Anders M; Djurovic, Srdjan; Agartz, Ingrid; Westlye, Lars T; Stefansson, Hreinn; Stefansson, Kari; Thompson, Paul M; Andreassen, Ole A., [Importance] Recurrent microdeletions and duplications in the genomic region 15q11.2 between breakpoints 1 (BP1) and 2 (BP2) are associated with neurodevelopmental disorders. These structural variants are present in 0.5% to 1.0% of the population, making 15q11.2 BP1-BP2 the site of the most prevalent known pathogenic copy number variation (CNV). It is unknown to what extent this CNV influences brain structure and affects cognitive abilities., [Objective] To determine the association of the 15q11.2 BP1-BP2 deletion and duplication CNVs with cortical and subcortical brain morphology and cognitive task performance., [Design, Setting, and Participants] In this genetic association study, T1-weighted brain magnetic resonance imaging were combined with genetic data from the ENIGMA-CNV consortium and the UK Biobank, with a replication cohort from Iceland. In total, 203 deletion carriers, 45 247 noncarriers, and 306 duplication carriers were included. Data were collected from August 2015 to April 2019, and data were analyzed from September 2018 to September 2019., [Main Outcomes and Measures] The associations of the CNV with global and regional measures of surface area and cortical thickness as well as subcortical volumes were investigated, correcting for age, age2, sex, scanner, and intracranial volume. Additionally, measures of cognitive ability were analyzed in the full UK Biobank cohort., [Results] Of 45 756 included individuals, the mean (SD) age was 55.8 (18.3) years, and 23 754 (51.9%) were female. Compared with noncarriers, deletion carriers had a lower surface area (Cohen d = −0.41; SE, 0.08; P = 4.9 × 10−8), thicker cortex (Cohen d = 0.36; SE, 0.07; P = 1.3 × 10−7), and a smaller nucleus accumbens (Cohen d = −0.27; SE, 0.07; P = 7.3 × 10−5). There was also a significant negative dose response on cortical thickness (β = −0.24; SE, 0.05; P = 6.8 × 10−7). Regional cortical analyses showed a localization of the effects to the frontal, cingulate, and parietal lobes. Further, cognitive ability was lower for deletion carriers compared with noncarriers on 5 of 7 tasks., [Conclusions and Relevance] These findings, from the largest CNV neuroimaging study to date, provide evidence that 15q11.2 BP1-BP2 structural variation is associated with brain morphology and cognition, with deletion carriers being particularly affected. The pattern of results fits with known molecular functions of genes in the 15q11.2 BP1-BP2 region and suggests involvement of these genes in neuronal plasticity. These neurobiological effects likely contribute to the association of this CNV with neurodevelopmental disorders., This study is supported in part by grants U54 EB20403, R01MH116147, and R56AG058854 from the National Institutes of Health, grant 609020 from the European Union Seventh Framework Programme, and grants 223273 and 276082 from the Research Council Norway. Part of this work was performed using the Service for Sensitive Data (TSD), which is developed and operated by the TSD Service Groupand owned by the University of Oslo.

Aim: Subthreshold syndromes (STS) frequently precede the onset of full-threshold syndromes (FTS) of the corresponding mental disorder (homotypic continuity). This study examines whether subthreshold conditions are comorbid and whether there is heterotypic continuity also between STS and FTS. Methods: Data were extracted from the Brisbane “19Up” cohort study of twins and siblings (N = 1838; 56% female) on individuals who (i) completed self-report ratings of depression-like (DLE), hypomanic-like (HMLE) and psychotic-like experiences (PLE) and (ii) were assessed for mood and psychotic FTS using the Composite International Diagnostic Interview (CIDI). Associations between STS and FTS were estimated using adjusted prevalence ratios (APR) and 95% confidence intervals (CI). Results: STS are prevalent, with 22% reporting DLE, 14% HMLE and 5% PLE; 7% reported >1 STS, with PLE most likely to demonstrate comorbidity. Individuals with DLE were likely to meet CIDI criteria for depression (APR: 3.71, 95% CI: 2.83, 4.89), hypo/mania (APR: 3.62, 95% CI: 2.21, 5.94) and psychotic disorders (APR: 1.74, 95% CI 1.08, 2.83). Individuals with HMLE were likely to meet CIDI criteria for depression (APR: 2.29, 95% CI: 1.58, 3.31) and hypo/mania (APR: 2.51, 95% CI: 1.29, 4.91); those with PLE were likely to meet criteria for hypo/mania (APR: 5.8, 95% CI: 1.90, 17.70) and psychotic disorders (APR: 17.27, 95% CI: 7.54, 39.65). Conclusions: The findings suggest that STS are common among young adults and show heterotypic as well as homotypic continuity with FTS and support the need for more trans-diagnostic research on the evolution of major mental disorders.

Summary Fingerprints are of long-standing practical and cultural interest, but little is known about the mechanisms that underlie their variation. Using genome-wide scans in Han Chinese cohorts, we identified 18 loci associated with fingerprint type across the digits, including a genetic basis for the long-recognized “pattern-block” correlations among the middle three digits. In particular, we identified a variant near EVI1 that alters regulatory activity and established a role for EVI1 in dermatoglyph patterning in mice. Dynamic EVI1 expression during human development supports its role in shaping the limbs and digits, rather than influencing skin patterning directly. Trans-ethnic meta-analysis identified 43 fingerprint-associated loci, with nearby genes being strongly enriched for general limb development pathways. We also found that fingerprint patterns were genetically correlated with hand proportions. Taken together, these findings support the key role of limb development genes in influencing the outcome of fingerprint patterning., Graphical abstract, Highlights • GWAS identifies variants associated with fingerprint type across all digits • Fingerprint-associated genes are strongly enriched for limb development functions • Evi1 alters dermatoglyphs in mice by modulating limb rather than skin development • Fingerprint patterns are genetically correlated with hand and finger proportions, Genome-wide association scans in Han Chinese individuals and a trans-ethnic meta-analysis reveal genetic regions that are associated with specific fingerprint patterning; functional studies in mouse models confirm a role of EVI1 in early limb development and ridge patterning.

Contains fulltext : 248656.pdf (Publisher’s version ) (Closed access) BACKGROUND: Sex differences in incidence and/or presentation of schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BIP) are pervasive. Previous evidence for shared genetic risk and sex differences in brain abnormalities across disorders suggest possible shared sex-dependent genetic risk. METHODS: We conducted the largest to date genome-wide genotype-by-sex (G×S) interaction of risk for these disorders using 85,735 cases (33,403 SCZ, 19,924 BIP, and 32,408 MDD) and 109,946 controls from the PGC (Psychiatric Genomics Consortium) and iPSYCH. RESULTS: Across disorders, genome-wide significant single nucleotide polymorphism-by-sex interaction was detected for a locus encompassing NKAIN2 (rs117780815, p = 3.2 × 10(-8)), which interacts with sodium/potassium-transporting ATPase (adenosine triphosphatase) enzymes, implicating neuronal excitability. Three additional loci showed evidence (p < 1 × 10(-6)) for cross-disorder G×S interaction (rs7302529, p = 1.6 × 10(-7); rs73033497, p = 8.8 × 10(-7); rs7914279, p = 6.4 × 10(-7)), implicating various functions. Gene-based analyses identified G×S interaction across disorders (p = 8.97 × 10(-7)) with transcriptional inhibitor SLTM. Most significant in SCZ was a MOCOS gene locus (rs11665282, p = 1.5 × 10(-7)), implicating vascular endothelial cells. Secondary analysis of the PGC-SCZ dataset detected an interaction (rs13265509, p = 1.1 × 10(-7)) in a locus containing IDO2, a kynurenine pathway enzyme with immunoregulatory functions implicated in SCZ, BIP, and MDD. Pathway enrichment analysis detected significant G×S interaction of genes regulating vascular endothelial growth factor receptor signaling in MDD (false discovery rate-corrected p < .05). CONCLUSIONS: In the largest genome-wide G×S analysis of mood and psychotic disorders to date, there was substantial genetic overlap between the sexes. However, significant sex-dependent effects were enriched for genes related to neuronal development and immune and vascular functions across and within SCZ, BIP, and MDD at the variant, gene, and pathway levels.

Altered structural brain asymmetry in autism spectrum disorder (ASD) has been reported. However, findings have been inconsistent, likely due to limited sample sizes. Here we investigated 1,774 individuals with ASD and 1,809 controls, from 54 independent data sets of the ENIGMA consortium. ASD was significantly associated with alterations of cortical thickness asymmetry in mostly medial frontal, orbitofrontal, cingulate and inferior temporal areas, and also with asymmetry of orbitofrontal surface area. These differences generally involved reduced asymmetry in individuals with ASD compared to controls. Furthermore, putamen volume asymmetry was significantly increased in ASD. The largest case-control effect size was Cohen's d = -0.13, for asymmetry of superior frontal cortical thickness. Most effects did not depend on age, sex, IQ, severity or medication use. Altered lateralized neurodevelopment may therefore be a feature of ASD, affecting widespread brain regions with diverse functions. Large-scale analysis was necessary to quantify subtle alterations of brain structural asymmetry in ASD.

Reproductive longevity is essential for fertility and influences healthy ageing in women1,2, but insights into its underlying biological mechanisms and treatments to preserve it are limited. Here we identify 290 genetic determinants of ovarian ageing, assessed using normal variation in age at natural menopause (ANM) in about 200,000 women of European ancestry. These common alleles were associated with clinical extremes of ANM; women in the top 1% of genetic susceptibility have an equivalent risk of premature ovarian insufficiency to those carrying monogenic FMR1 premutations3. The identified loci implicate a broad range of DNA damage response (DDR) processes and include loss-of-function variants in key DDR-associated genes. Integration with experimental models demonstrates that these DDR processes act across the life-course to shape the ovarian reserve and its rate of depletion. Furthermore, we demonstrate that experimental manipulation of DDR pathways highlighted by human genetics increases fertility and extends reproductive life in mice. Causal inference analyses using the identified genetic variants indicate that extending reproductive life in women improves bone health and reduces risk of type 2 diabetes, but increases the risk of hormone-sensitive cancers. These findings provide insight into the mechanisms that govern ovarian ageing, when they act, and how they might be targeted by therapeutic approaches to extend fertility and prevent disease. Hundreds of genetic loci associated with age at menopause, combined with experimental evidence in mice, highlight mechanisms of reproductive ageing across the lifespan.

Objective Genetic factors underpin the narrow intraindividual variability of thyroid function, although precise contributions of environmental vs genetic factors remain uncertain. We sought to clarify the heritability of thyroid function traits and thyroid peroxidase antibody (TPOAb) positivity and identify single nucleotide polymorphisms (SNPs) contributing to the trait variance. Methods Heritability of thyroid-stimulating hormone (TSH), free T4 (fT4), free T3 (fT3) and TPOAb in a cohort of 2854 euthyroid, dizygous and monozygous twins (age range 11.9–16.9 years) from the Brisbane Longitudinal Twin Study (BLTS) was assessed using structural equation modelling. A genome-wide analysis was conducted on 2832 of these individuals across 7 522 526 SNPs as well as gene-based association analyses. Replication analysis of the association results was performed in the Raine Study (n = 1115) followed by meta-analysis to maximise power for discovery. Results Heritability of thyroid function parameters in the BLTS was 70.8% (95% CI: 66.7–74.9%) for TSH, 67.5% (59.8–75.3%) for fT4, 59.7% (54.4–65.0%) for fT3 and 48.8% (40.6–56.9%) for TPOAb. The genome-wide association study (GWAS) in the discovery cohort identified a novel association between rs2026401 upstream of NCOA3 and TPOAb. GWAS meta-analysis found associations between TPOAb and rs445219, also near NCOA3, and fT3 and rs12687280 near SERPINA7. Gene-based association analysis highlighted SERPINA7 for fT3 and NPAS3 for fT4. Conclusion Our findings resolve former contention regarding heritability estimates of thyroid function traits and TPOAb positivity. GWAS and gene-based association analysis identified variants accounting for a component of this heritability.

Objectives The heterogeneity and comorbidity of major mental disorders presenting in adolescents and young adults has fostered calls for trans-diagnostic research. This study examines early expressions of psychopathology and risk and trans-diagnostic caseness in a community cohort of twins and non-twin siblings. Methods Using data from the Brisbane Longitudinal Twin Study, we estimated median number of self-rated psychiatric symptoms, prevalence of subthreshold syndromes, family history of mood and/or psychotic disorders, and likelihood of subsequent trans-diagnostic caseness (individuals meeting diagnostic criteria for mood and/or psychotic syndromes). Next, we used cross-validated Chi-Square Automatic Interaction Detector (CHAID) analyses to identify the nature and relative importance of individual self-rated symptoms that predicted trans-diagnostic caseness. We examined the positive and negative predictive values (PPV; NPV) and accuracy of all classifications (Area under the Curve and 95% confidence intervals: AUC; 95% CI). Results Of 1815 participants (Female 1050, 58%; mean age 26.40), more than one in four met caseness criteria for a mood and/or psychotic disorder. Examination of individual factors indicated that the AUC was highest for subthreshold syndromes, followed by family history then self-rated psychiatric symptoms, and that NPV always exceeded PPV for caseness. In contrast, the CHAID analysis (adjusted for age, sex, twin status) generated a classification tree comprising six trans-diagnostic symptoms. Whilst the contribution of two symptoms (need for sleep; physical activity) to the model was more difficult to interpret, CHAID analysis indicated that four self-rated symptoms (sadness; feeling overwhelmed; impaired concentration; paranoia) offered the best discrimination between cases and non-cases. These four symptoms showed different associations with family history status. Conclusions The findings need replication in independent cohorts. However, the use of CHAID might provide a means of identifying specific subsets of trans-diagnostic symptoms representing clinical phenotypes that predict transition to caseness in individuals at risk of onset of major mental disorders.

This study examined the extent to which psychosocial impact of nausea and vomiting during pregnancy predicts postpartum depression using a retrospective design. Data from a cross-sectional survey investigating women’s experiences of nausea and vomiting during pregnancy were used ( N = 861). Hierarchical logistic regression models revealed that the psychosocial impact of nausea and vomiting in pregnancy appears to be predictive of postpartum depression, independent of depression status before and during pregnancy. Our findings indicate that assessing the psychosocial impact of nausea and vomiting in pregnancy during antenatal care may identify women at risk of postpartum depression.

This study assessed the heritability of 25 hydroxyvitamin D3 (25(OH)D3) in a large twin cohort and the shared effect of sun exposure and skin colour on 25(OH)D3 variance. Study participants included 1604 twin pairs and their siblings (n = 4020). Twin correlations for 25(OH)D3 concentration were rMZ=0.79 (584 pairs) and rDZ = 0.52 (1020 pairs) consistent with an average h2 = 0.50 throughout the year. Significant phenotypic and genetic seasonal fluctuation was observed in 25(OH)D3 concentrations with heritability decreasing during the winter (h2 = 0.37) compared to summer (h2 = 0.62). Skin colour (measured both ordinally and quantitatively) and self-reported sun exposure were found to significantly affect 25(OH)D3 concentration. Twins with olive/dark skin had significantly lower 25(OH)D3 concentrations than those with fair/pale skin and multivariate genetic analysis showed that approximately half of the total additive genetic variation in 25(OH)D3 results from genes whose primary influence is on skin colour and sun exposure. Additionally, 37% of the total variance was attributed to shared environmental effects on vitamin D, skin colour and sun exposure measures. These results support a moderate estimate of vitamin D heritability and suggest significant influence of season, skin colour and sun exposure on the genetic variance.

ObjectivesThis study sought to evaluate the prevalence, timing of onset and duration of symptoms of depression in the perinatal period (PND) in women with depression, according to whether they had a history of depression prior to their first perinatal period. We further sought to identify biopsychosocial correlates of perinatal symptoms in women with depression.Design and settingThe Australian Genetics of Depression Study is an online case cohort study of the aetiology of depression. For a range of variables, women with depression who report significant perinatal depressive symptoms were compared with women with lifetime depression who did not experience perinatal symptoms.ParticipantsIn a large sample of parous women with major depressive disorder (n=7182), we identified two subgroups of PND cases with and without prior depression history (n=2261; n=878, respectively).Primary and secondary outcome measuresThe primary outcome measure was a positive screen for PND on the lifetime version of the Edinburgh Postnatal Depression Scale. Descriptive measures reported lifetime prevalence, timing of onset and duration of PND symptoms. There were no secondary outcome measures.ResultsThe prevalence of PND among parous women was 70%. The majority of women reported at least one perinatal episode with symptoms both antenatally and postnatally. Of women who experienced depression prior to first pregnancy, PND cases were significantly more likely to report more episodes of depression (OR=1.15 per additional depression episode, 95% CI 1.13 to 1.17, pConclusionsThe majority of parous women with lifetime depression in this study experienced PND, associated with more complex, severe depression. Results highlight the importance of perinatal assessments of depressive symptoms, particularly for women with a history of depression or childhood adverse experiences.

Special Issue: The ENIGMA Consortium: the first 10 years.-- Karolinska Schizophrenia Project (KaSP)., Age has a major effect on brain volume. However, the normative studies available are constrained by small sample sizes, restricted age coverage and significant methodological variability. These limitations introduce inconsistencies and may obscure or distort the lifespan trajectories of brain morphometry. In response, we capitalized on the resources of the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) Consortium to examine age-related trajectories inferred from cross-sectional measures of the ventricles, the basal ganglia (caudate, putamen, pallidum, and nucleus accumbens), the thalamus, hippocampus and amygdala using magnetic resonance imaging data obtained from 18,605 individuals aged 3–90 years. All subcortical structure volumes were at their maximum value early in life. The volume of the basal ganglia showed a monotonic negative association with age thereafter; there was no significant association between age and the volumes of the thalamus, amygdala and the hippocampus (with some degree of decline in thalamus) until the sixth decade of life after which they also showed a steep negative association with age. The lateral ventricles showed continuous enlargement throughout the lifespan. Age was positively associated with inter-individual variability in the hippocampus and amygdala and the lateral ventricles. These results were robust to potential confounders and could be used to examine the functional significance of deviations from typical age-related morphometric patterns., National Institute of Mental Health, Grant/Award Numbers: MH104284, MH116147, R01MH113619, R01 MH090553, R01MH117014, R01MH042191; Karolinska Institutet; Stockholm County Council; Southern and Eastern Norway Regional Health Authority; German Centre for Cardiovascular Research; DZHK; Siemens Healthineers; University of Queensland; US National Institute of Child Health and Human Development, Grant/Award Number: RO1HD050735; Australian National Health and Medical Research Council; Eunice Kennedy Shriver National Institute of Child Health & Human Development; National Institute on Drug Abuse, Grant/Award Numbers: UL1 TR000153, 1 U24 RR025736-01, 1 U24 RR021992; Brain Injury Fund Research Grant Program; Indiana State Department of Health Spinal Cord; Parents Against Childhood Epilepsy; Epilepsy Therapy Project, Fight Against Childhood Epilepsy and Seizures; Epilepsy Foundation; American Epilepsy Society; Knut and Alice Wallenberg Foundation; European Community's Horizon 2020 Programme; Vici Innovation Program; NWO Brain & Cognition Excellence Program; Netherlands Organization for Scientific Research; Hersenstichting Nederland; Netherlands Organization for Health Research and Development; Geestkracht Programme of the Dutch Health Research Council, Grant/Award Number: 10-000-1001; Universiteit Utrecht; FP7 Ideas: European Research Council; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; National Center for Advancing Translational Sciences; National Institutes of Health; National Center for Research Resources; Consortium grant, Grant/Award Number: U54 EB020403; U.S. National Institutes of Health, Grant/Award Numbers: R01 CA101318, P30 AG10133, R01 AG19771; Medical Research Council, Grant/Award Number: G0500092; Fundación Instituto de Investigación Marqués de Valdecilla, Grant/Award Numbers: API07/011, NCT02534363, NCT0235832; Instituto de Salud Carlos III, Grant/Award Numbers: PI14/00918, PI14/00639, PI060507, PI050427, PI020499; the Research Council, Grant/Award Number: 223273; South Eastern Norway Regional Health Authority, Grant/Award Numbers: 2017-112, 2019107; Swedish Research Council; European Community's Seventh Framework Programme, Grant/Award Number: 602450; King's College London; South London and Maudsley NHS Foundation Trust; Biomedical Research Centre; National Institute for Health Research.

The Geschwind-Behan-Galaburda and sexual differentiation models predict an association between elevated foetal androgen exposure and left-handedness whereas the callosal hypothesis predicts the opposite. We present a meta-analysis of correlations between handedness and digit ratio (2D:4D), a putative marker of prenatal testosterone. Left-handedness predicted low (male-typical) right-hand digit ratio (R2D:4D), high (female-typical) left-hand digit ratio (L2D:4D), and low R2D:4D-L2D:4D directional asymmetry (D[R-L]). Effect sizes were extremely small and not moderated by sex or method of measuring handedness or 2D:4D. The same general pattern was observed after excluding the very large study (110,329 males, 90,412 females) of Manning and Peters ([2009]. Digit ratio (2D:4D) and hand preference for writing in the BBC Internet Study. Laterality: Asymmetries of Body, Brain and Cognition, 14(5), 528-540. doi:10.1080/13576500802637872); however, no significant effects for R2D:4D were observed once these samples were removed. The results do not confirm any theory linking prenatal androgens with handedness, so we speculate they instead reflect the mechanical action of writing causing subtle changes in the musculature and/or fat pads of the fingers. Gripping a pen/pencil might cause an increase in 2D relative to 4D (and/or decrease in 4D relative to 2D) resulting in higher ratios on the writing-hand; furthermore, this could differ between left- and right-handers due to writing in the left-to-right direction (as in English) having asymmetrical effects depending on which hand is used.

OBJECTIVES: Network analysis is increasingly applied to psychopathology research. We used it to examine the core phenomenology of emerging bipolar disorder (BD I and II) and ‘at risk’ presentations (major depression with a family history of BD). METHODOLOGY: The study sample comprised a community cohort of 1867 twin and non-twin siblings (57% female; mean age ~26) who had completed self-report ratings of (i) depression-like, hypomanic-like and psychotic-like experiences; (ii) family history of BD; and (iii) were assessed for mood and psychotic syndromes using the Composite International Diagnostic Interview (CIDI). Symptom networks were compared for recent onset BD versus other cohort members and then for individuals at risk of BD (depression with/without a family history of BD). RESULTS: The four key symptoms that differentiated recent onset BD from other cohort members were: anergia, psychomotor speed, hypersomnia and (less) loss of confidence. The four key symptoms that differentiated individuals at high risk of BD from unipolar depression were: anergia, psychomotor speed, impaired concentration, and hopelessness. However, the latter network was less stable and more error prone. CONCLUSIONS: We are encouraged by the overlaps between our findings and those from two recent publications reporting network analyses of BD psychopathology, especially as the studies recruited from different populations and employed different network models. However, the advantages of applying network analysis to youth mental health cohorts (which include many individuals with multi-morbidity) must be weighed against the disadvantages including basic issues such as judgements regarding the selection of items for inclusion in network models.

Childhood aggressive behavior (AGG) has a substantial heritability of around 50%. Here we present a genome-wide association meta-analysis (GWAMA) of childhood AGG, in which all phenotype measures across childhood ages from multiple assessors were included. We analyzed phenotype assessments for a total of 328 935 observations from 87 485 children aged between 1.5 and 18 years, while accounting for sample overlap. We also meta-analyzed within subsets of the data, i.e., within rater, instrument and age. SNP-heritability for the overall meta-analysis (AGGoverall) was 3.31% (SE = 0.0038). We found no genome-wide significant SNPs for AGGoverall. The gene-based analysis returned three significant genes: ST3GAL3 (P = 1.6E–06), PCDH7 (P = 2.0E–06), and IPO13 (P = 2.5E–06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children (variance explained = 0.44%) and in retrospectively assessed childhood aggression (variance explained = 0.20%). Genetic correlations (rg) among rater-specific assessment of AGG ranged from rg = 0.46 between self- and teacher-assessment to rg = 0.81 between mother- and teacher-assessment. We obtained moderate-to-strong rgs with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range $$\left| {r_g} \right|$$ r g : 0.19–1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (rg = ~−0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range $$\left| {r_g} \right|$$ r g : 0.46–0.60). The genetic correlations between aggression and psychiatric disorders were weaker for teacher-reported AGG than for mother- and self-reported AGG. The current GWAMA of childhood aggression provides a powerful tool to interrogate the rater-specific genetic etiology of AGG.

Handedness, a consistent asymmetry in skill or use of the hands, has been studied extensively because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank, 23andMe and 32 studies from the International Handedness Consortium, we conducted the world’s largest genome-wide association study of handedness (1,534,836 right-handed, 194,198 (11.0%) left-handed and 37,637 (2.1%) ambidextrous individuals). We found 41 genetic loci associated with left-handedness and seven associated with ambidexterity at genome-wide levels of significance (P < 5×10−8). Tissue enrichment analysis implicated the central nervous system and brain tissues including the hippocampus and cerebrum in the etiology of left-handedness. Pathways including regulation of microtubules, neurogenesis, axonogenesis and hippocampus morphology were also highlighted. We found suggestive positive genetic correlations between being left-handed and some neuropsychiatric traits including schizophrenia and bipolar disorder. SNP heritability analyses indicated that additive genetic effects of genotyped variants explained 5.9% (95% CI = 5.8% – 6.0%) of the underlying liability of being left-handed, while the narrow sense heritability was estimated at 12% (95% CI = 7.2% – 17.7%). Further, we show that genetic correlation between left-handedness and ambidexterity is low (rg = 0.26; 95% CI = 0.08 – 0.43) implying that these traits are largely influenced by different genetic mechanisms. In conclusion, our findings suggest that handedness, like many other complex traits is highly polygenic, and that the genetic variants that predispose to left-handedness may underlie part of the association with some psychiatric disorders that has been observed in multiple observational studies.

ENIGMA-CNV working group., Low-frequency 1q21.1 distal deletion and duplication copy number variant (CNV) carriers are predisposed to multiple neurodevelopmental disorders, including schizophrenia, autism and intellectual disability. Human carriers display a high prevalence of micro- and macrocephaly in deletion and duplication carriers, respectively. The underlying brain structural diversity remains largely unknown. We systematically called CNVs in 38 cohorts from the large-scale ENIGMA-CNV collaboration and the UK Biobank and identified 28 1q21.1 distal deletion and 22 duplication carriers and 37,088 non-carriers (48% male) derived from 15 distinct magnetic resonance imaging scanner sites. With standardized methods, we compared subcortical and cortical brain measures (all) and cognitive performance (UK Biobank only) between carrier groups also testing for mediation of brain structure on cognition. We identified positive dosage effects of copy number on intracranial volume (ICV) and total cortical surface area, with the largest effects in frontal and cingulate cortices, and negative dosage effects on caudate and hippocampal volumes. The carriers displayed distinct cognitive deficit profiles in cognitive tasks from the UK Biobank with intermediate decreases in duplication carriers and somewhat larger in deletion carriers—the latter potentially mediated by ICV or cortical surface area. These results shed light on pathobiological mechanisms of neurodevelopmental disorders, by demonstrating gene dose effect on specific brain structures and effect on cognitive function., 1000BRAINS: The 1000BRAINS study was funded by the Institute of Neuroscience and Medicine, Research Center Juelich, Germany. We thank the Heinz Nixdorf Foundation (Germany) for the generous support of the Heinz Nixdorf Recall Study on which 1000BRAINS is based. We also thank the scientists and the study staff of the Heinz Nixdorf Recall Study and 1000BRAINS. Funding was also granted by the Initiative and Networking Fund of the Helmholtz Association (Caspers) and the European Union’s Horizon 2020 Research and Innovation Programme under Grant Agreement 945539 (Human Brain Project SGA3; Amunts, Caspers, Cichon). Brainscale: The Brainscale study was supported by the Netherlands Organization for Scientific Research MagW 480-04-004 (Dorret I. Boomsma), 51.02.060 (Hilleke E. Hulshoff Pol), 668.772 (Dorret I. Boomsma and Hilleke E. Hulshoff Pol); NWO/SPI 56-464-14192 (Dorret I. Boomsma), the European Research Council (ERC-230374) (Dorret I. Boomsma), High Potential Grant Utrecht University (Hilleke E.Hulshoff Pol) and NWO Brain and Cognition 433-09-220 (Hilleke E.Hulshoff Pol). Betula: The Betula study was funded by the Knut and Alice Wallenberg (KAW) foundation (Nyberg). The Freesurfer segmentations on the Betula sample were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC) at HPC2N (in Umeå, Sweden), partially funded by the Swedish Research Council through grant agreement no. 2018-05973. Brain Imaging Genetics (BIG): This work makes use of the BIG database, first established in Nijmegen, The Netherlands, in 2007. This resource is now part of Cognomics (www.cognomics.nl), a joint initiative by researchers from the Donders Centre for Cognitive Neuroimaging, the Human Genetics and Cognitive Neuroscience departments of the Radboud University Medical Centre and the Max Planck Institute for Psycholinguistics in Nijmegen. The Cognomics Initiative has received support from the participating departments and centres and from external grants, that is, the Biobanking and Biomolecular Resources Research Infrastructure (Netherlands) (BBMRI-NL), the Hersenstichting Nederland and the Netherlands Organization for Scientific Research (NWO). The research leading to these results also receives funding from the NWO Gravitation grant ‘Language in Interaction’, the European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement nos. 602450 (IMAGEMEND), 278948 (TACTICS) and 602805 (Aggressotype), as well as from the European Community’s Horizon 2020 programme under grant agreement no. 643051 (MiND) and from ERC-2010-AdG 268800-NEUROSCHEMA. In addition, the work was supported by a grant for the ENIGMA Consortium (grant number U54 EB020403) from the BD2K Initiative of a cross-NIH partnership. deCODE genetics: deCODE genetics acknowledges support from the Innovative Medicines Initiative Joint Undertaking under grant agreement nos. 115008 (NEWMEDS) and 115300 (EUAIMS), of which resources are composed of EFPIA in-kind contribution and financial contribution from the European Union’s Seventh Framework Programme (EU-FP7/2007-2013), EU-FP7-funded grant agreement no. 602450 (IMAGEMEND) and EU-funded FP7-People-2011-IAPP grant agreement no. 286213 (PsychDPC). Dublin: This work was supported by Science Foundation Ireland (SFI grant 12/IP/1359 to Gary Donohoe and grant SFI08/IN.1/B1916-Corvin to Aidan C. Corvin). ECHO-DEFINE: The ECHO study acknowledges funding from a Medical Research Council (MRC) Centre Grant to Michael J. Owen (G0801418), the Wellcome Trust (Institutional Strategic Support Fund (ISSF) to van den Bree and Clinical Research Training Fellowship to Joanne L. Doherty), the Waterloo Foundation (WF 918-1234 to van den Bree), the Baily Thomas Charitable Fund (2315/1 to van den Bree), National Institute of Mental Health (NIMH 5UO1MH101724 to van den Bree and Michael J. Owen), the IMAGINE-2 study (funded by the MRC (MR/T033045/1) to van den Bree, Jeremy Hall and Michael J. Owen), the IMAGINE-ID study (funded by MRC (MR/N022572/1) to Jeremy Hall, van den Bree and Owen). The DEFINE study was supported by a Wellcome Trust Strategic Award (100202/Z/12/Z) to Michael J. Owen. ENIGMA: ENIGMA is supported in part by NIH grants U54 EB20403, R01MH116147 and R56AG058854. NIA T32AG058507; NIH/NIMH 5T32MH073526. EPIGEN-Dublin: The EPIGEN-Dublin cohort was supported by a Science Foundation Ireland Research Frontiers Programme award (08/RFP/GEN1538). EPIGEN-UK (Sisodiya): The work was partly undertaken at UCLH/UCL, which received a proportion of funding from the UK Department of Health’s NIHR Biomedical Research Centres funding scheme. We are grateful to the Wolfson Trust and the Epilepsy Society for supporting the Epilepsy Society MRI scanner. GAP: This work was supported by the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and the Institute of Psychiatry, Psychology and Neuroscience, King’s College London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. GOBS: The GOBS study data collection was supported in part by the National Institutes of Health (NIH) grants: R01 MH078143, R01 MH078111 and R01 MH083824, with work conducted in part in facilities constructed under the support of NIH grant C06 RR020547. GSP: Data were in part provided by the Brain Genomics Superstruct Project (GSP) of Harvard University and Massachusetts General Hospital (MGH) (Principal Investigators: Randy Buckner, Jordan Smoller and Joshua Roffman), with support from the Center for Brain Science Neuroinformatics Research Group, Athinoula A. Martinos Center for Biomedical Imaging, Center for Genomic Medicine and Stanley Center for Psychiatric Research. Twenty individual investigators at Harvard and MGH generously contributed data to the overall project. We would like to thank Randy Buckner for insightful comments and feedback on this work. HUBIN: The HUBIN study was financed by the Swedish Research Council (K2010-62X-15078-07-2, K2012-61X-15078-09-3, 521-2014-3487 K2015-62X-15077-12-3, 2017-00949), the regional agreement on medical training and clinical research between Stockholm County Council and the Karolinska Institutet. HUNT: The HUNT study is a collaboration between HUNT Research Centre (Faculty of Medicine and Movement Sciences, NTNU—Norwegian University of Science and Technology), Nord-Trøndelag County Council, Central Norway Health Authority and the Norwegian Institute of Public Health. HUNT-MRI was funded by the Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology, and the Norwegian National Advisory Unit for functional MRI. IMAGEN: This work received support from the following sources: the European Union-funded FP6 Integrated Project IMAGEN (reinforcement-related behaviour in normal brain function and psychopathology) (LSHM-CT- 2007-037286), the Horizon 2020 funded ERC Advanced Grant ‘STRATIFY’ (Brain network based stratification of reinforcement-related disorders) (695313), ERANID (Understanding the Interplay between Cultural, Biological and Subjective Factors in Drug Use Pathways) (PR-ST-0416-10004), BRIDGET (JPND: BRain Imaging, cognition Dementia and next generation GEnomics) (MR/N027558/1), Human Brain Project (HBP SGA 2, 785907),the FP7 projects IMAGEMEND(602450; IMAging GEnetics for MENtal Disorders) and MATRICS (603016), the Innovative Medicine Initiative Project EUAIMS (115300-2), the Medical Research Council Grant ‘c-VEDA’ (Consortium on Vulnerability to Externalizing Disorders and Addictions) (MR/N000390/1), the Swedish Research Council FORMAS, the Medical Research Council, the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London, the Bundesministeriumfür Bildung und Forschung (BMBF grants 01GS08152, 01EV0711; eMED SysAlc01ZX1311A; Forschungsnetz AERIAL 01EE1406A, 01EE1406B), the Deutsche Forschungsgemeinschaft (DFG grants, SM 80/7-2, SFB 940/2), the Medical Research Foundation and Medical Research Council (grants MR/R00465X/1 and MR/S020306/1). Further support was provided by grants from: ANR (project AF12-NEUR0008-01—WM2NA, ANR-12-SAMA-0004), the Eranet Neuron (ANR-18-NEUR00002-01), the Fondation de France (00081242), the Fondation pour la Recherche Médicale (DPA20140629802), the Mission Interministérielle de Lutte-contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the Assistance-Publique-Hôpitaux-de-Paris and INSERM (interface grant), Paris Sud University IDEX 2012, the Fondation de l’Avenir (grant AP-RM-17-013), the Fédération pour la Recherche sur le Cerveau; the National Institutes of Health, Science Foundation Ireland (16/ERCD/3797), USA (Axon, Testosterone and Mental Health during Adolescence; RO1 MH085772-01A1) and by NIH Consortium grant U54 EB020403, supported by a cross-NIH alliance that funds Big Data to Knowledge Centres of Excellence. Lifespan: The study is funded by the Research Council of Norway (230345, 288083 and 223273). NCNG: NCNG sample collection was supported by grants from the Bergen Research Foundation and the University of Bergen, the Dr Einar Martens Fund, the Research Council of Norway, to le Hellard, Steen and Espeseth. The Bergen group was supported by grants from the Western Norway Regional Health Authority (Grant 911593 to Arvid Lundervold, Grant 911397 and 911687 to Astri Johansen Lundervold). NTR: The NTR cohort was supported by the Netherlands Organization for Scientific Research (NWO) and The Netherlands Organisation for Health Research and Development (ZonMW) grants 904-61-090, 985-10-002, 912-10-020, 904-61-193, 480-04-004,463-06-001, 451-04-034, 400-05-717, Addiction-31160008, 016-115-035, 481-08-011, 056-32-010, Middelgroot-911-09-032, OCW_NWO Gravity programme—024.001.003, NWO-Groot 480-15-001/674, Center for Medical Systems Biology (CSMB, NWO Genomics), NBIC/BioAssist/RK(2008.024), Biobanking and Biomolecular Resources Research Infrastructure (BBMRI-NL, 184.021.007 and 184.033.111); Spinozapremie (NWO-56-464-14192), KNAW Academy Professor Award (PAH/6635) and University Research Fellow grant (URF) to Dorret I. Boomsma; Amsterdam Public Health research institute (former EMGO+), Neuroscience Amsterdam research institute (former NCA); the European Science Foundation (ESF, EU/QLRT-2001-01254), the European Community’s Seventh Framework Programme (FP7- HEALTH-F4-2007-2013, grant 01413: ENGAGE and grant 602768: ACTION); the European Research Council (ERC Starting 284167, ERC Consolidator 771057, ERC Advanced 230374), Rutgers University Cell and DNA Repository (NIMH U24 MH068457-06), the National Institutes of Health (NIH, R01D0042157-01A1, R01MH58799-03, MH081802, DA018673, R01 DK092127-04, Grand Opportunity grants 1RC2 MH089951 and 1RC2 MH089995); the Avera Institute for Human Genetics, Sioux Falls, South Dakota (USA). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. Computing was supported by NWO through grant 2018/EW/00408559, BiG Grid, the Dutch e-Science Grid and SURFSARA. OATS: The OATS study has been funded by a National Health & Medical Research Council (NHMRC) and Australian Research Council (ARC) Strategic Award Grant of the Ageing Well, Ageing Productively Programme (ID No. 401162) and NHMRC Project Grants (ID Nos. 1045325 and 1085606). This research was facilitated through Twins Research Australia, a national resource in part supported by an NHMRC Centre for Research Excellence Grant (ID No.: 1079102). We thank the participants for their time and generosity in contributing to this research. We acknowledge the contribution of the OATS research team (https://cheba.unsw.edu.au/project/older-australian-twins-study) to this study. OATS genotyping was partly funded by a Commonwealth Scientific and Industrial Research Organization Flagship Collaboration Fund Grant. Osaka: Osaka study was supported by the Brain Mapping by Integrated Neurotechnologies for Disease Studies (Brain/MINDS: Grant Number JP18dm0207006), Brain/MINDS& beyond studies (Grant Number JP20dm0307002) and Health and Labour Sciences Research Grants for Comprehensive Research on Persons with Disabilities (Grant Number JP20dk0307081) from the Japan Agency for Medical Research and Development (AMED), Grants-in-Aid for Scientific Research (KAKENHI; Grant Numbers JP25293250 and JP16H05375). Some computations were performed at the Research Center for Computational Science, Okazaki, Japan. PAFIP: The PAFIP study was supported by Instituto de Salud Carlos III, FIS 00/3095, 01/3129, PI020499, PI060507, PI10/00183, the SENY Fundació Research Grant CI2005-0308007 and the FundaciónMarqués de Valdecilla API07/011. Biological samples from our cohort were stored at the Valdecilla Biobank and genotyping services were conducted at the Spanish ‘Centro Nacional de Genotipado’ (CEGEN-ISCIII). MCIC/COBRE: The study is funded by the National Institutes of Health studies R01EB006841, P20GM103472 and P30GM122734 and Department of Energy DE-FG02-99ER62764. PING: Data collection and sharing for the Paediatric Imaging, Neurocognition and Genetics (PING) Study (National Institutes of Health Grant RC2DA029475) were funded by the National Institute on Drug Abuse and the Eunice Kennedy Shriver National Institute of Child Health & Human Development. A full list of PING investigators is at http://pingstudy.ucsd.edu/investigators.html. QTIM: The QTIM study was supported by the National Institute of Child Health and Human Development (R01 HD050735) and the National Health and Medical Research Council (NHMRC 486682, 1009064), Australia. Genotyping was supported by NHMRC (389875). Medland is supported in part by an NHMRC fellowship (APP1103623). SHIP: SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grant nos. 01ZZ9603, 01ZZ0103 and 01ZZ0403), the Ministry of Cultural Affairs and the Social Ministry of the Federal State of Mecklenburg-West Pomerania. Genome-wide single-nucleotide polymorphism typing in SHIP and MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from Siemens Healthineers, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. StrokeMRI: StrokeMRI was supported by the Norwegian ExtraFoundation for Health and Rehabilitation(2015/FO5146), the Research Council of Norway (249795, 262372), the South-Eastern Norway Regional Health Authority (2014097, 2015044, 2015073) and the Department of Psychology, University of Oslo. Sydney MAS: The Sydney Memory and Aging Study (Sydney MAS) is funded by National and HealthMedical Research Council (NHMRC) Programme and Project Grants (ID350833, ID568969 and ID109308). We also thank the Sydney MAS participants and the Research Team. SYS: The SYS Study is supported by Canadian Institutes of Health Research. TOP: Centre of Excellence: RCN #23273 and RCN #226971. Part of this work was performed on the TSD (Tjeneste for Sensitive Data) facilities, owned by the University of Oslo, operated and developed by the TSD service group at the University of Oslo, IT-Department (USIT) (tsd-drift@usit.uio.no). The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7-PEOPLE-2013-COFUND) under grant agreement no. 609020—Scientia Fellows; the Research Council of Norway (RCN) #276082—A lifespan perspective on mental illness: toward precision medicine using multimodal brain imaging and genetics. Ida E. Sønderby and Rune Bøen are supported by South-Eastern Norway Regional Health Authority (#2020060). Ida E. Sønderby and Ole A. Andreassen have received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement no. 847776 (CoMorMent project) and the KG Jebsen Foundation (SKGJ-MED-021). UCLA_UMCU: The UCLA_UMCU cohort comprises of six studies which were supported by National Alliance for Research in Schizophrenia and Affective Disorders (NARSAD) (20244 to Prof. Hillegers), The Netherlands Organisation for Health Research and Development (ZonMw) (908-02-123 to Prof. Hulshoff Pol), and Netherlands Organisation for Scientific Research (NWO 9120818 and NWO-VIDI 917-46-370 to Prof. Hulshoff Pol). The GROUP study was funded through the Geestkracht programme of the Dutch Health Research Council (ZonMw, grant number 10-000-1001), and matching funds from participating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly and Janssen Cilag) and universities and mental health care organizations (Amsterdam: Academic Psychiatric Centre of the Academic Medical Center and the mental health institutions: GGZ inGeest, Arkin, Dijk en Duin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord-Holland-Noord. Groningen: University Medical Center Groningen and the mental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dimence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassia Psycho-medical Center, The Hague. Maastricht: Maastricht University Medical Centre and the mental health institutions: GGzE, GGZ Breburg, GGZ Oost-Brabant, Vincent van Gogh, voor Geestelijke Gezondheid, Mondriaan, Virenzeriagg, Zuyderland GGZ, MET ggz, Universitair Centrum Sint-JozefKortenberg, CAPRI University of Antwerp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZ Overpelt, OPZ Rekem. Utrecht: University Medical Center Utrecht and the mental health institutions: Altrecht, GGZ Centraal and Delta.). UK Biobank: This work made use of data sharing from UK Biobank (under project code 27412). Others: Work by Pierre Vanderhaeghen was funded by Grants of the European Research Council (ERC Adv Grant GENDEVOCORTEX), the EOS Programme, the Belgian FWO, the AXA Research Fund and the Belgian Queen Elizabeth Foundation. Ikuo K. Suzuki was supported by a postdoctoral fellowship of the FRS/FNRS.

Miscarriage is a common, complex trait affecting ~15% of clinically confirmed pregnancies. Here we present the results of large-scale genetic association analyses with 69,054 cases from five different ancestries for sporadic miscarriage, 750 cases of European ancestry for multiple (≥3) consecutive miscarriage, and up to 359,469 female controls. We identify one genome-wide significant association (rs146350366, minor allele frequency (MAF) 1.2%, P = 3.2 × 10−8, odds ratio (OR) = 1.4) for sporadic miscarriage in our European ancestry meta-analysis and three genome-wide significant associations for multiple consecutive miscarriage (rs7859844, MAF = 6.4%, P = 1.3 × 10−8, OR = 1.7; rs143445068, MAF = 0.8%, P = 5.2 × 10−9, OR = 3.4; rs183453668, MAF = 0.5%, P = 2.8 × 10−8, OR = 3.8). We further investigate the genetic architecture of miscarriage with biobank-scale Mendelian randomization, heritability, and genetic correlation analyses. Our results show that miscarriage etiopathogenesis is partly driven by genetic variation potentially related to placental biology, and illustrate the utility of large-scale biobank data for understanding this pregnancy complication., Miscarriage affects around 15% of clinically confirmed pregnancies. Here the authors carry out a large genome-wide association study for sporadic and multiple consecutive miscarriage and suggest links with placental biology.

We investigated the heritability of educational attainment and how it differed between birth cohorts and cultural-geographic regions. A classical twin design was applied to pooled data from 28 cohorts representing 16 countries and including 193,518 twins with information on educational attainment at 25 years of age or older. Genetic factors explained the major part of individual differences in educational attainment (heritability: a(2)=0.43; 0.41-0.44), but also environmental variation shared by co-twins was substantial (c(2)=0.31; 0.30-0.33). The proportions of educational variation explained by genetic and shared environmental factors did not differ between Europe, North America and Australia, and East Asia. When restricted to twins 30 years or older to confirm finalized education, the heritability was higher in the older cohorts born in 1900-1949 (a(2)=0.44; 0.41-0.46) than in the later cohorts born in 1950-1989 (a(2)=0.38; 0.36-0.40), with a corresponding lower influence of common environmental factors (c(2)=0.31; 0.29-0.33 and c(2)=0.34; 0.32-0.36, respectively). In conclusion, both genetic and environmental factors shared by co-twins have an important influence on individual differences in educational attainment. The effect of genetic factors on educational attainment has decreased from the cohorts born before to those born after the 1950s. This study was conducted within the CODATwins project. We thank Wendy Cozen, University of Southern California, for sharing data with us. Funding for collaborators: Netherlands Twin Register acknowledges the Netherlands Organization for Scientific Research (NWO) and MagW/ZonMW grants 904-61-090, 985-10-002, 912-10-020, 904-61-193,480-04-004, 463-06-001, 451-04-034, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192; VU University's Institute for Health and Care Research (EMGO+); the European Research Council (ERC - 230374), the Avera Institute, Sioux Falls, South Dakota (USA). Data collection and analyses in Finnish twin cohorts have been supported by ENGAGE - European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007, Grant Agreement Number 201413, National Institute of Alcohol Abuse and Alcoholism (Grants AA-12502, AA-00145, and AA-09203 to R J Rose, the Academy of Finland Center of Excellence in Complex Disease Genetics (Grant Numbers: 213506, 129680), and the Academy of Finland (Grants 100499, 205585, 118555, 141054, 265240, 263278 and 264146 to J Kaprio). Since its origin the East Flanders Prospective Survey has been partly supported by grants from the Fund of Scientific Research, Flanders and Twins, a non-profit Association for Scientific Research in Multiple Births (Belgium). Anthropometric measurements of the Hungarian twins were supported by Medexpert Ltd., Budapest, Hungary. The Murcia Twin Registry is supported by Fundacion Seneca, Regional Agency for Science and Technology, Murcia, Spain (08633/PHCS/08, 15302/PHCS/10 & 19479/PI/14) and Ministry of Science and Innovation, Spain (PSI2009-11560 & PSI2014-56680-R). K Silventoinen is supported by Osaka University's International Joint Research Promotion Program. We acknowledge The Swedish Twin Registry for access to data. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council under the grant no 2017-00641. This research was facilitated through access to Twins Research Australia, a national resource supported by a Centre of Research Excellence Grant (ID: 1079102), from the National Health and Medical Research Council. California Twin Program was supported by The California Tobacco-Related Disease Research Program (7RT-0134H, 8RT-0107H, 6RT-0354H) and the National Institutes of Health (1R01ESO15150-01). The Carolina African American Twin Study of Aging (CAATSA) was funded by a grant from the National Institute on Aging (grant 1RO1-AG13662-01A2) to K. E. Whitfield. Colorado Twin Registry is funded by NIDA funded center grant DA011015, & Longititudinal Twin Study HD10333; Author Huibregtse is supported by 5T32DA017637 and 5T32AG052371. Washington State Twin Registry (formerly the University of Washington Twin Registry) was supported in part by grant NIH RC2 HL103416 (D. Buchwald, PI). Vietnam Era Twin Study of Aging was supported by National Institute of Health grants NIA R01 AG018384, R01 AG018386, R01 AG022381, and R01 AG022982, and, in part, with resources of the VA San Diego Center of Excellence for Stress and Mental Health. The Cooperative Studies Program of the Office of Research & Development of the United States Department of Veterans Affairs has provided financial support for the development and maintenance of the Vietnam Era Twin (VET) Registry. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the NIA/NIH, or the VA. The NAS-NRC Twin Registry acknowledges financial support from the National Institutes of Health grant number R21 AG039572. Korean Twin-Family Register was supported by the Global Research Network Program of the National Research Foundation (NRF 2011-220-E00006). Osaka University Aged Twin Registry is supported by grants from JSPS KAKENHI JP (23593419, 24792601, 26671010, 24590695, 26293128, 16K15385, 16K15978, 16K15989, 16H03261).

Background Insomnia may predict onset of mental disorders in adults. However, it is unclear whether the same directional relationship exists during the peak age range for the onset of major mental disorders and/or whether other types of sleep-wake disturbance, such as hypersomnia, show similar associations. Methods Longitudinal follow-up of >1800 community residing twins and non-twin siblings (mean age ~26; 57% female). Adjusted relative risk ratios and 95% confidence intervals (Adj RR and 95% CI) were estimated for onset of depression, hypomania and psychosis in individuals with prior self-reported exposure to Insomnia and/or Hypersomnia or proxies for insomnia disorder (Insomnia and Daytime Impairment) and atypical symptom profile (Hypersomnia and Anergia). Results Risk of onset differed somewhat according to type of syndrome and the nature of sleep-wake disturbance (e.g. Insomnia alone increased risk of first onset of psychosis). Overall, the risk for onset of any syndrome was best identified using composite measures (Adj RR were ~1.5–2.5) such as Insomnia and Hypersomnia, Insomnia and Daytime Impairment, or Hypersomnia and Anergia, rather than singular items describing night-time disruption only. Conclusions The magnitude of risk of onset of major mental health problems and the availability of effective, low-cost, individual and population-based interventions for sleep-wake disturbances, suggest that it is justifiable to introduce screening for and strategies to overcome sleep problems in youth.

This study assessed genetic contributions to six cognitive domains, identified by the MATRICS Cognitive Consensus Battery as relevant for schizophrenia, cognition-enhancing, clinical trials. Psychiatric Genomics Consortium Schizophrenia polygenic risk scores showed significant negative correlations with each cognitive domain. Genome-wide association analyses identified loci associated with attention/vigilance (rs830786 within HNF4G), verbal memory (rs67017972 near NDUFS4), and reasoning/problem solving (rs76872642 within HDAC9). Gene set analysis identified unique and shared genes across cognitive domains. These findings suggest involvement of common and unique mechanisms across cognitive domains and may contribute to the discovery of new therapeutic targets to treat cognitive deficits in schizophrenia.

BackgroundThe heterogeneity of genetic effects on major depressive disorder (MDD) may be partly attributable to moderation of genetic effects by environment, such as exposure to childhood trauma (CT). Indeed, previous findings in two independent cohorts showed evidence for interaction between polygenic risk scores (PRSs) and CT, albeit in opposing directions. This study aims to meta-analyze MDD-PRS × CT interaction results across these two and other cohorts, while applying more accurate PRSs based on a larger discovery sample.MethodsData were combined from 3024 MDD cases and 2741 control subjects from nine cohorts contributing to the MDD Working Group of the Psychiatric Genomics Consortium. MDD-PRS were based on a discovery sample of ∼110,000 independent individuals. CT was assessed as exposure to sexual or physical abuse during childhood. In a subset of 1957 cases and 2002 control subjects, a more detailed five-domain measure additionally included emotional abuse, physical neglect, and emotional neglect.ResultsMDD was associated with the MDD-PRS (odds ratio [OR] = 1.24, p = 3.6 × 10-5, R2 = 1.18%) and with CT (OR = 2.63, p = 3.5 × 10-18 and OR = 2.62, p = 1.4 ×10-5 for the two- and five-domain measures, respectively). No interaction was found between MDD-PRS and the two-domain and five-domain CT measure (OR = 1.00, p = .89 and OR = 1.05, p = .66).ConclusionsNo meta-analytic evidence for interaction between MDD-PRS and CT was found. This suggests that the previously reported interaction effects, although both statistically significant, can best be interpreted as chance findings. Further research is required, but this study suggests that the genetic heterogeneity of MDD is not attributable to genome-wide moderation of genetic effects by CT.

Intelligence is highly heritable 1 and a major determinant of human health and well-being 2 . Recent genome-wide meta-analyses have identified 24 genomic loci linked to variation in intelligence 3-7 , but much about its genetic underpinnings remains to be discovered. Here, we present a large-scale genetic association study of intelligence (n = 269,867), identifying 205 associated genomic loci (190 new) and 1,016 genes (939 new) via positional mapping, expression quantitative trait locus (eQTL) mapping, chromatin interaction mapping, and gene-based association analysis. We find enrichment of genetic effects in conserved and coding regions and associations with 146 nonsynonymous exonic variants. Associated genes are strongly expressed in the brain, specifically in striatal medium spiny neurons and hippocampal pyramidal neurons. Gene set analyses implicate pathways related to nervous system development and synaptic structure. We confirm previous strong genetic correlations with multiple health-related outcomes, and Mendelian randomization analysis results suggest protective effects of intelligence for Alzheimer's disease and ADHD and bidirectional causation with pleiotropic effects for schizophrenia. These results are a major step forward in understanding the neurobiology of cognitive function as well as genetically related neurological and psychiatric disorders.

BackgroundMany studies have identified changes in the brain associated with obsessive–compulsive disorder (OCD), but few have examined the relationship between genetic determinants of OCD and brain variation.AimsWe present the first genome-wide investigation of overlapping genetic risk for OCD and genetic influences on subcortical brain structures.MethodUsing single nucleotide polymorphism effect concordance analysis, we measured genetic overlap between the first genome-wide association study (GWAS) of OCD (1465 participants with OCD, 5557 controls) and recent GWASs of eight subcortical brain volumes (13 171 participants).ResultsWe found evidence of significant positive concordance between OCD risk variants and variants associated with greater nucleus accumbens and putamen volumes. When conditioning OCD risk variants on brain volume, variants influencing putamen, amygdala and thalamus volumes were associated with risk for OCD.ConclusionsThese results are consistent with current OCD neurocircuitry models. Further evidence will clarify the relationship between putamen volume and OCD risk, and the roles of the detected variants in this disorder.Declaration of interestThe authors have declared that no competing interests exist.

Oscillatory activity is crucial for information processing in the brain, and has a long history as a biomarker for psychopathology. Variation in oscillatory activity is highly heritable, but the involvement of specific genetic variants, genes, and brain expression pathways remains elusive. Here, we present a genome-wide association study (GWAS) for the power of oscillations at standard frequencies (~2 Hz delta, ~6 Hz theta, ~10 Hz alpha, and ~20 Hz beta) of the electroencephalogram (EEG), followed up with gene-based and brain-expression analyses based on the extraction of RNA expression quantitative trait loci (eQTL) and imputation of gene expression in brain tissues by machine learning (Metaxcan). Five cohorts with eyes-closed resting EEG and genome-wide single nucleotide polymorphism (SNP) were included with data for 8425 participants. GWAS revealed a significant SNP for alpha oscillation power intronic to protein-coding gene PRKG2. PRKG2 is amongst the genes deleted in the 4q21 microdeletion syndrome which results in speech and mental retardation. GABRA2 -- a known genetic marker for alcohol use disorder and epilepsy -- significantly affected beta power, consistent with the relation between GABAA interneuron activity and beta oscillations, and between beta oscillations and alcohol use disorders. Twenty-four genes in region 3p21.1 -- previously linked to schizophrenia -- reached significance for alpha power, which fits well with observed aberrant oscillatory activity in schizophrenia. SNPs in this region were eQTLs for GLYCTK in hippocampal tissue, and eQTLs for GNL3 and ITIH4 in the frontal cortex. SNP-based imputation of hippocampal GABRA2 expression was significantly associated with beta oscillations and indicated associations of immune genes IL18R1 and IL1RL1 in the Hippocampus and Putamen with alpha oscillation strength. In conclusion, we successfully associated genes and genetic variants with oscillatory brain activity, some of which were previously associated with psychopathology. The results show how psychopathological liability genes affect brain functioning via cortical and subcortical brain expression.

Importance: Urban life has been proposed as an environmental risk factor accounting for the increased prevalence of schizophrenia in urban areas. An alternative hypothesis is that individuals with increased genetic risk tend to live in urban/dense areas.Objective: To assess whether adults with higher genetic risk for schizophrenia have an increased probability to live in more populated areas than those with lower risk.Design, Setting, and Participants: Four large, cross-sectional samples of genotyped individuals of European ancestry older than 18 years with known addresses in Australia, the United Kingdom, and the Netherlands were included in the analysis. Data were based on the postcode of residence at the time of last contact with the participants. Community-based samples who took part in studies conducted by the Queensland Institute for Medical Research Berghofer Medical Research Institute (QIMR), UK Biobank (UKB), Netherlands Twin Register (NTR), or QSkin Sun and Health Study (QSKIN) were included. Genome-wide association analysis and mendelian randomization (MR) were included. The study was conducted between 2016 and 2018.Exposures: Polygenic risk scores for schizophrenia derived from genetic data (genetic risk is independently measured from the occurrence of the disease). Socioeconomic status of the area was included as a moderator in some of the models.Main Outcomes and Measures: Population density of the place of residence of the participants determined from census data. Remoteness and socioeconomic status of the area were also tested.Results: The QIMR participants (15 544; 10 197 [65.6%] women; mean [SD] age, 54.4 [13.2] years) living in more densely populated areas (people per square kilometer) had a higher genetic loading for schizophrenia (r2 = 0.12%; P = 5.69 × 10-5), a result that was replicated across all 3 other cohorts (UKB: 345 246; 187 469 [54.3%] women; age, 65.7 [8.0] years; NTR: 11 212; 6727 [60.0%] women; age, 48.6 [17.5] years; and QSKIN: 15 726; 8602 [54.7%] women; age, 57.0 [7.9] years). This genetic association could account for 1.7% (95% CI, 0.8%-3.2%) of the schizophrenia risk. Estimates from MR analyses performed in the UKB sample were significant (b = 0.049; P = 3.7 × 10-7 using GSMR), suggesting that the genetic liability to schizophrenia may have a causal association with the tendency to live in urbanized locations.Conclusions and Relevance: The results of this study appear to support the hypothesis that individuals with increased genetic risk tend to live in urban/dense areas and suggest the need to refine the social stress model for schizophrenia by including genetics as well as possible gene-environment interactions.

Major depressive disorder (MDD) is a common illness accompanied by considerable morbidity, mortality, costs, and heightened\ud risk of suicide. We conducted a genome-wide association meta-analysis based in 135,458 cases and 344,901 controls and identified\ud 44 independent and significant loci. The genetic findings were associated with clinical features of major depression and\ud implicated brain regions exhibiting anatomical differences in cases. Targets of antidepressant medications and genes involved\ud in gene splicing were enriched for smaller association signal. We found important relationships of genetic risk for major depression\ud with educational attainment, body mass, and schizophrenia: lower educational attainment and higher body mass were\ud putatively causal, whereas major depression and schizophrenia reflected a partly shared biological etiology. All humans carry\ud lesser or greater numbers of genetic risk factors for major depression. These findings help refine the basis of major depression\ud and imply that a continuous measure of risk underlies the clinical phenotype.

Hair color is one of the most recognizable visual traits in European populations and is under strong genetic control. Here we report the results of a genome-wide association study meta-analysis of almost 300,000 participants of European descent. We identified 123 autosomal and one X-chromosome loci significantly associated with hair color; all but 13 are novel. Collectively, single-nucleotide polymorphisms associated with hair color within these loci explain 34.6% of red hair, 24.8% of blond hair, and 26.1% of black hair heritability in the study populations. These results confirm the polygenic nature of complex phenotypes and improve our understanding of melanin pigment metabolism in humans.

Background Sex differences in rates of depression emerge during adolescence. However, it is unclear whether symptom patterns and trajectories differ significantly according to gender in youth. Barriers to research include the fact that most self-report tools are weighted towards psychological rather than somatic symptoms. Methods Data were collected on symptoms of depression in about 1800 individuals at ages 12, 14 and 16 years. Odds ratios and 95% confidence intervals were used to examine the trajectory of psychological and somatic phenotypes and self-reported depression caseness over time. Results At age 12, 24% of participants met criteria for self-reported depression caseness. Although there was only a small incremental increase in the prevalence over time (about 5%), 57% of participants met criteria for self-reported depression caseness at least once. Generic symptoms at age 12 were associated with depression longitudinally, although early transition to caseness was reported in females only. Categorization as a psychological phenotype at age 12 predicted depression at age 14 and/or 16 years, especially in females. The somatic phenotype was more common in males, but showed a weaker association with self-reported depression caseness over time. Limitations Depression was assessed by self-report; only 30% of participants had ratings for age 12, 14 and 16. Conclusions Although sub-threshold psychological and somatic syndromes often co-occur in cases of self-reported depression in adolescence, longitudinally they may represent independent symptom trajectories. However, it is important to remember that self-reported depression is indicative of, but not confirmation of a depressive episode that meets diagnostic criteria.

Research on environmental and genetic pathways to complex traits such as educational attainment (EA) is confounded by uncertainty over whether correlations reflect effects of transmitted parental genes, causal family environments, or some, possibly interactive, mixture of both. Thus, an aggregate of thousands of alleles associated with EA (a polygenic risk score; PRS) may tap parental behaviors and home environments promoting EA in the offspring. New methods for unpicking and determining these causal pathways are required. Here, we utilize the fact that parents pass, at random, 50% of their genome to a given offspring to create independent scores for the transmitted alleles (conventional EA PRS) and a parental score based on allelesnot transmittedto the offspring (EA VP_PRS). The formal effect of non-transmitted alleles on offspring attainment was tested in 2,333 genotyped twins for whom high-quality measures of EA, assessed at age 17 years, were available, and whose parents were also genotyped. Four key findings were observed. First, the EA PRS and EA VP_PRS were empirically independent, validating the virtual-parent design. Second, in this family-based design, children's own EA PRS significantly predicted their EA (β = 0.15), ruling out stratification confounds as a cause of the association of attainment with the EA PRS. Third, parental EA PRS predicted the SES environment parents provided to offspring (β = 0.20), and parental SES and offspring EA were significantly associated (β = 0.33). This would suggest that the EA PRS is at least as strongly linked to social competence as it is to EA, leading to higher attained SES in parents and, therefore, a higher experienced SES for children. In a full structural equation model taking account of family genetic relatedness across multiple siblings the non-transmitted allele effects were estimated at similar values; but, in this more complex model, confidence intervals included zero. A test using the forthcoming EA3 PRS may clarify this outcome. The virtual-parent method may be applied to clarify causality in other phenotypes where observational evidence suggests parenting may moderate expression of other outcomes, for instance in psychiatry.

BackgroundRecent analyses of trait-disorder overlap suggest that psychiatric dimensions may relate to distinct sets of genes that exert their maximum influence during different periods of development. This includes analyses of social-communciation difficulties that share, depending on their developmental stage, stronger genetic links with either Autism Spectrum Disorder or schizophrenia. Here we developed a multivariate analysis framework in unrelated individuals to model directly the developmental profile of genetic influences contributing to complex traits, such as social-communication difficulties, during a ∼10-year period spanning childhood and adolescence.MethodsLongitudinally assessed quantitative social-communication problems (N≤ 5,551) were studied in participants from a UK birth cohort (ALSPAC, 8 to 17 years). Using standardised measures, genetic architectures were investigated with novel multivariate genetic-relationship-matrix structural equation models (GSEM) incorporating whole-genome genotyping information. Analogous to twin research, GSEM included Cholesky decomposition, common pathway and independent pathway models.ResultsA 2-factor Cholesky decomposition model described the data best. One genetic factor was common to SCDC measures across development, the other accounted for independent variation at 11 years and later, consistent with distinct developmental profiles in trait-disorder overlap. Importantly, genetic factors operating at 8 years explained only ∼50% of the genetic variation at 17 years.ConclusionUsing latent factor models, we identified developmental changes in the genetic architecture of social-communication difficulties that enhance the understanding of ASD and schizophrenia-related dimensions. More generally, GSEM present a framework for modelling shared genetic aetiologies between phenotypes and can provide prior information with respect to patterns and continuity of trait-disorder overlap.

We recently reported an association of offspring educational attainment with polygenic risk scores (PRS) computed on parent’s non-transmitted alleles for educational attainment using the second GWAS meta-analysis article on educational attainment published by the Social Science Genetic Association Consortium. Here we test the replication of these findings using a more powerful PRS from the third GWAS meta-analysis article by the Consortium. Each of the key findings of our previous paper is replicated using this improved PRS (N = 2335 adolescent twins and their genotyped parents). The association of children’s attainment with their own PRS increased substantially with the standardized effect size, moving from β = 0.134, 95% CI = 0.079, 0.188 for EA2, to β = 0.223, 95% CI = 0.169, 0.278, p < .001, for EA3. Parent’s PRS again predicted the socioeconomic status (SES) they provided to their offspring and increased from β = 0.201, 95% CI = 0.147, 0.256 to β = 0.286, 95% CI = 0.239, 0.333. Importantly, the PRS for alleles not transmitted to their offspring — therefore acting via the parenting environment — was increased in effect size from β = 0.058, 95% CI = 0.003, 0.114 to β = 0.067, 95% CI = 0.012, 0.122, p = .016. As previously found, this non-transmitted genetic effect was fully accounted for by parental SES. The findings reinforce the conclusion that genetic effects of parenting are substantial, explain approximately one-third the magnitude of an individual’s own genetic inheritance and are mediated by parental socioeconomic competence.

Imaging genetics analyses use neuroimaging traits as intermediate phenotypes to infer the degree of genetic contribution to brain structure and function in health and/or illness. Coefficients of relatedness (CR) summarize the degree of genetic similarity among subjects and are used to estimate the heritability – the proportion of phenotypic variance explained by genetic factors. The CR can be inferred directly from genome-wide genotype data to explain the degree of shared variation in common genetic polymorphisms (SNP-heritability) among related or unrelated subjects. We developed a central processing and graphics processing unit (CPU and GPU) accelerated Fast and Powerful Heritability Inference (FPHI) approach that linearizes likelihood calculations to overcome the ∼N2–3 computational effort dependency on sample size of classical likelihood approaches. We calculated for 60 regional and 1.3 × 105 voxel-wise traits in N = 1,206 twin and sibling participants from the Human Connectome Project (HCP) (550 M/656 F, age = 28.8 ± 3.7 years) and N = 37,432 (17,531 M/19,901 F; age = 63.7 ± 7.5 years) participants from the UK Biobank (UKBB). The FPHI estimates were in excellent agreement with heritability values calculated using Genome-wide Complex Trait Analysis software (r = 0.96 and 0.98 in HCP and UKBB sample) while significantly reducing computational (102–4 times). The regional and voxel-wise traits heritability estimates for the HCP and UKBB were likewise in excellent agreement (r = 0.63–0.76, p < 10−10). In summary, the hardware-accelerated FPHI made it practical to calculate heritability values for voxel-wise neuroimaging traits, even in very large samples such as the UKBB. The patterns of additive genetic variance in neuroimaging traits measured in a large sample of related and unrelated individuals showed excellent agreement regardless of the estimation method. The code and instruction to execute these analyses are available at www.solar-eclipse-genetics.org.

IMPORTANCE: Genetic studies with broad definitions of depression may not capture genetic risk specific to major depressive disorder (MDD), raising questions about how depression should be operationalized in future genetic studies. OBJECTIVE: To use a large, well-phenotyped single study of MDD to investigate how different definitions of depression used in genetic studies are associated with estimation of MDD and phenotypes of MDD, using polygenic risk scores (PRSs). DESIGN, SETTING, AND PARTICIPANTS: In this case-control polygenic risk score analysis, patients meeting diagnostic criteria for a diagnosis of MDD were drawn from the Australian Genetics of Depression Study, a cross-sectional, population-based study of depression, and controls and patients with self-reported depression were drawn from QSkin, a population-based cohort study. Data analyzed herein were collected before September 2018, and data analysis was conducted from September 10, 2020, to January 27, 2021. MAIN OUTCOME AND MEASURES: Polygenic risk scores generated from genome-wide association studies using different definitions of depression were evaluated for estimation of MDD in and within individuals with MDD for an association with age at onset, adverse childhood experiences, comorbid psychiatric and somatic disorders, and current physical and mental health. RESULTS: Participants included 12 106 (71% female; mean age, 42.3 years; range, 18-88 years) patients meeting criteria for MDD and 12 621 (55% female; mean age, 60.9 years; range, 43-87 years) control participants with no history of psychiatric disorders. The effect size of the PRS was proportional to the discovery sample size, with the largest study having the largest effect size with the odds ratio for MDD (1.75; 95% CI, 1.73-1.77) per SD of PRS and the PRS derived from ICD-10 codes documented in hospitalization records in a population health cohort having the lowest odds ratio (1.14; 95% CI, 1.12-1.16). When accounting for differences in sample size, the PRS from a genome-wide association study of patients meeting diagnostic criteria for MDD and control participants was the best estimator of MDD, but not in those with self-reported depression, and associations with higher odds ratios with childhood adverse experiences and measures of somatic distress. CONCLUSIONS AND RELEVANCE: These findings suggest that increasing sample sizes, regardless of the depth of phenotyping, may be most informative for estimating risk of depression. The next generation of genome-wide association studies should, like the Australian Genetics of Depression Study, have both large sample sizes and extensive phenotyping to capture genetic risk factors for MDD not identified by other definitions of depression.

Neuroticism, a ‘Big Five’ personality trait, has been associated with sub-clinical traits of both autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD). The objective of the current study was to examine whether causal overlap between ASD and ADHD traits can be accounted for by genetic and environmental risk factors that are shared with neuroticism. We performed twin-based structural equation modeling using self-report data from 12 items of the Neo Five-Factor Inventory Neuroticism domain, 11 Social Responsiveness Scale items, and 12 Adult ADHD Self-Report Scale items obtained from 3,170 young adult Australian individual twins (1,081 complete pairs). Univariate analysis for neuroticism, ASD, and ADHD traits suggested that the most parsimonious models were those with additive genetic and unique environmental components, without sex limitation effects. Heritability of neuroticism, ASD, and ADHD traits, as measured by these methods, was moderate (between 40% and 45% for each respective trait). In a trivariate model, we observed moderate phenotypic (between 0.45 and 0.62), genetic (between 0.56 and 0.71), and unique environmental correlations (between 0.37and 0.55) among neuroticism, ASD, and ADHD traits, with the highest value for the shared genetic influence between neuroticism and self-reported ASD traits (rg = 0.71). Together, our results suggest that in young adults, genetic, and unique environmental risk factors indexed by neuroticism overlap with those that are shared by ASD and ADHD.

The diathesis-stress theory for depression states that the effects of stress on the depression risk are dependent on the diathesis or vulnerability, implying multiplicative interactive effects on the liability scale. We used polygenic risk scores for major depressive disorder (MDD) calculated from the results of the most recent analysis from the Psychiatric Genomics Consortium as a direct measure of the vulnerability for depression in a sample of 5221 individuals from 3083 families. In the same we also had measures of stressful life events and social support and a depression symptom score, as well as DSM-IV MDD diagnoses for most individuals. In order to estimate the variance in depression explained by the genetic vulnerability, the stressors and their interactions, we fitted linear mixed models controlling for relatedness for the whole sample as well as stratified by sex. We show a significant interaction of the polygenic risk scores with personal life events (0.12% of variance explained, P-value=0.0076) contributing positively to the risk of depression. Additionally, our results suggest possible differences in the aetiology of depression between women and men. In conclusion, our findings point to an extra risk for individuals with combined vulnerability and high number of reported personal life events beyond what would be expected from the additive contributions of these factors to the liability for depression, supporting the multiplicative diathesis-stress model for this disease.

BackgroundChildbirth is a potent trigger for the onset of psychiatric illness in women including postpartum depression (PPD) and postpartum psychosis (PP). Medical complications occurring during pregnancy and/or childbirth have been linked to postpartum psychiatric illness and sociodemographic factors. We evaluated if pregnancy and obstetrical predictors have similar effects on different types of postpartum psychiatric disorders.MethodA population-based cohort study using Danish registers was conducted in 392 458 primiparous women with a singleton delivery between 1995 and 2012 and no previous psychiatric history. The main outcome was first-onset postpartum psychiatric episodes. Incidence rate ratios (IRRs) were calculated for any psychiatric contact in four quarters for the first year postpartum.ResultsPPD and postpartum acute stress reactions were associated with pregnancy and obstetrical complications. For PPD, hyperemesis gravidarum [IRR 2.69, 95% confidence interval (CI) 1.93–3.73], gestational hypertension (IRR 1.84, 95% CI 1.33–2.55), pre-eclampsia (IRR 1.45, 95% CI 1.14–1.84) and Cesarean section (C-section) (IRR 1.32, 95% CI 1.13–1.53) were associated with increased risk. For postpartum acute stress, hyperemesis gravidarum (IRR 1.93, 95% CI 1.38–2.71), preterm birth (IRR 1.51, 95% CI 1.30–1.75), gestational diabetes (IRR 1.42, 95% CI 1.03–1.97) and C-section (IRR 1.36, 95% CI 1.20–1.55) were associated with increased risk. In contrast, risk of PP was not associated with pregnancy or obstetrical complications.ConclusionsPregnancy and obstetrical complications can increase the risk for PPD and acute stress reactions but not PP. Identification of postpartum women requiring secondary care is needed to develop targeted approaches for screening and treatment. Future work should focus on understanding the contributions of psychological stressors and underlying biology on the development of postpartum psychiatric illness.

Major mood disorders, which primarily include bipolar disorder and major depressive disorder, are the leading cause of disability worldwide and pose a major challenge in identifying robust risk genes. Here, we present data from independent large-scale clinical data sets (including 29 557 cases and 32 056 controls) revealing brain expressed protocadherin 17 (PCDH17) as a susceptibility gene for major mood disorders. Single-nucleotide polymorphisms (SNPs) spanning the PCDH17 region are significantly associated with major mood disorders; subjects carrying the risk allele showed impaired cognitive abilities, increased vulnerable personality features, decreased amygdala volume and altered amygdala function as compared with non-carriers. The risk allele predicted higher transcriptional levels of PCDH17 mRNA in postmortem brain samples, which is consistent with increased gene expression in patients with bipolar disorder compared with healthy subjects. Further, overexpression of PCDH17 in primary cortical neurons revealed significantly decreased spine density and abnormal dendritic morphology compared with control groups, which again is consistent with the clinical observations of reduced numbers of dendritic spines in the brains of patients with major mood disorders. Given that synaptic spines are dynamic structures which regulate neuronal plasticity and have crucial roles in myriad brain functions, this study reveals a potential underlying biological mechanism of a novel risk gene for major mood disorders involved in synaptic function and related intermediate phenotypes.

Hyperemesis Gravidarum (HG), severe nausea/vomiting in pregnancy (NVP), can cause poor maternal/fetal outcomes. Genetic predisposition suggests the genetic component is essential in discovering an etiology. We performed whole-exome sequencing of 5 families followed by analysis of variants in 584 cases/431 controls. Variants in RYR2 segregated with disease in 2 families. The novel variant L3277R was not found in any case/control. The rare variant, G1886S was more common in cases (p = 0.046) and extreme cases (p = 0.023). Replication of G1886S using Norwegian/Australian data was supportive. Common variants rs790899 and rs1891246 were significantly associated with HG and weight loss. Copy-number analysis revealed a deletion in a patient. RYR2 encodes an intracellular calcium release channel involved in vomiting, cyclic-vomiting syndrome, and is a thyroid hormone target gene. Additionally, RYR2 is a downstream drug target of Inderal, used to treat HG and CVS. Thus, herein we provide genetic evidence for a pathway and therapy for HG.

Background and AimsThe non‐medical use of over‐the‐counter or prescribed analgesics (NMUA) is a significant public health problem. Little is known about the genetic and environmental etiology of NMUA and how these risks relate to other classes of substance use and misuse. Our aims were to estimate the heritability NMUA and sources of genetic and environmental covariance with cannabis and nicotine use, cannabis and alcohol use disorders and nicotine dependence in Australian twins.DesignBiometrical genetic analyses or twin methods using structural equation univariate and multivariate modeling.SettingAustralia.ParticipantsA total of 2007 young adult twins [66% female; μage = 25.9, standard deviation (SD) = 3.6, range = 18–38] from the Brisbane Longitudinal Twin Study retrospectively assessed between 2009 and 2016.MeasurementsSelf‐reported NMUA (non‐opioid or opioid‐based), life‐time nicotine, cannabis and opioid use, DSM‐V cannabis and alcohol use disorders and the Fagerström Test for Nicotine Dependence.FindingsLife‐time NMUA was reported by 19.4% of the sample. Univariate heritability explained 46% [95% confidence interval (CI) = 0.29–0.57] of the risks in NMUA. Multivariate analyses revealed that NMUA is moderately associated genetically with cannabis (rg = 0.41) and nicotine (rg = 0.45) use and nicotine dependence (rg = 0.34). In contrast, the genetic correlations with cannabis (rg = 0.15) and alcohol (rg = 0.07) use disorders are weak.ConclusionsIn young male and female adults in Australia, the non‐medical use of over‐the‐counter or prescribed analgesics appears to have moderate heritability. NMUA is moderately associated with cannabis and nicotine use and nicotine dependence. Its genetic etiology is largely distinct from that of cannabis and alcohol use disorders.

Telomere shortening has been associated with multiple age-related diseases such as cardiovascular disease, diabetes, and dementia. However, the biological mechanisms responsible for these associations remain largely unknown. In order to gain insight into the metabolic processes driving the association of leukocyte telomere length (LTL) with age-related diseases, we investigated the association between LTL and serum metabolite levels in 7,853 individuals from seven independent cohorts. LTL was determined by quantitative polymerase chain reaction and the levels of 131 serum metabolites were measured with mass spectrometry in biological samples from the same blood draw. With partial correlation analysis, we identified six metabolites that were significantly associated with LTL after adjustment for multiple testing: lysophosphatidylcholine acyl C17:0 (lysoPC a C17:0, p-value = 7.1 × 10−6), methionine (p-value = 9.2 × 10−5), tyrosine (p-value = 2.1 × 10−4), phosphatidylcholine diacyl C32:1 (PC aa C32:1, p-value = 2.4 × 10−4), hydroxypropionylcarnitine (C3-OH, p-value = 2.6 × 10−4), and phosphatidylcholine acyl-alkyl C38:4 (PC ae C38:4, p-value = 9.0 × 10−4). Pathway analysis showed that the three phosphatidylcholines and methionine are involved in homocysteine metabolism and we found supporting evidence for an association of lipid metabolism with LTL. In conclusion, we found longer LTL associated with higher levels of lysoPC a C17:0 and PC ae C38:4, and with lower levels of methionine, tyrosine, PC aa C32:1, and C3-OH. These metabolites have been implicated in inflammation, oxidative stress, homocysteine metabolism, and in cardiovascular disease and diabetes, two major drivers of morbidity and mortality.

Characterized primarily by a low body-mass index, anorexia nervosa is a complex and serious illness 1, affecting 0.9–4% of women and 0.3% of men 2–4, with twin-based heritability estimates of 50–60% 5. Mortality rates are higher than those in other psychiatric disorders 6, and outcomes are unacceptably poor 7. Here we combine data from the Anorexia Nervosa Genetics Initiative (ANGI) 8,9 and the Eating Disorders Working Group of the Psychiatric Genomics Consortium (PGC-ED) and conduct a genome-wide association study of 16,992 cases of anorexia nervosa and 55,525 controls, identifying eight significant loci. The genetic architecture of anorexia nervosa mirrors its clinical presentation, showing significant genetic correlations with psychiatric disorders, physical activity, and metabolic (including glycemic), lipid and anthropometric traits, independent of the effects of common variants associated with body-mass index. These results further encourage a reconceptualization of anorexia nervosa as a metabo-psychiatric disorder. Elucidating the metabolic component is a critical direction for future research, and paying attention to both psychiatric and metabolic components may be key to improving outcomes.

Objective: Asymmetry is a subtle but pervasive aspect of the human brain, and it may be altered in several psychiatric conditions. MRI studies have shown subtle differences of brain anatomy between people with major depressive disorder and healthy control subjects, but few studies have specifically examined brain anatomical asymmetry in relation to this disorder, and results from those studies have remained inconclusive. At the functional level, some electroencephalography studies have indicated left fronto-cortical hypoactivity and right parietal hypoactivity in depressive disorders, so aspects of lateralized anatomy may also be affected. The authors used pooled individual-level data from data sets collected around the world to investigate differences in laterality in measures of cortical thickness, cortical surface area, and subcortical volume between individuals with major depression and healthy control subjects.Methods: The authors investigated differences in the laterality of thickness and surface area measures of 34 cerebral cortical regions in 2,256 individuals with major depression and 3,504 control subjects from 31 separate data sets, and they investigated volume asymmetries of eight subcortical structures in 2,540 individuals with major depression and 4,230 control subjects from 32 data sets. T-1-weighted MRI data were processedwith a single protocol using FreeSurfer and the Desikan-Killiany atlas. The large sample size provided 80% power to detect effects of the order of Cohen's d=0.1.Results: The largest effect size (Cohen's d) of major depression diagnosis was 0.085 for the thickness asymmetry of the superior temporal cortex, which was not significant after adjustment for multiple testing. Asymmetry measures were not significantly associated with medication use, acute compared with remitted status, first episode compared with recurrent status, or age at onset.Conclusions: Altered brain macro-anatomical asymmetry may be of little relevance to major depression etiology in most cases.

It is well established that higher cognitive ability is associated with larger brain size. However, individual variation in intelligence exists despite brain size and recent studies have shown that a simple unifactorial view of the neurobiology underpinning cognitive ability is probably unrealistic. Educational attainment (EA) is often used as a proxy for cognitive ability since it is easily measured, resulting in large sample sizes and, consequently, sufficient statistical power to detect small associations. This study investigates the association between three global (total surface area (TSA), intra-cranial volume (ICV) and average cortical thickness) and 34 regional cortical measures with educational attainment using a polygenic scoring (PGS) approach. Analyses were conducted on two independent target samples of young twin adults with neuroimaging data, from Australia (N ​= ​1097) and the USA (N ​= ​723), and found that higher EA-PGS were significantly associated with larger global brain size measures, ICV and TSA (R2 ​= ​0.006 and 0.016 respectively, p ​< ​0.001) but not average thickness. At the regional level, we identified seven cortical regions—in the frontal and temporal lobes—that showed variation in surface area and average cortical thickness over-and-above the global effect. These regions have been robustly implicated in language, memory, visual recognition and cognitive processing. Additionally, we demonstrate that these identified brain regions partly mediate the association between EA-PGS and cognitive test performance. Altogether, these findings advance our understanding of the neurobiology that underpins educational attainment and cognitive ability, providing focus points for future research.

The aim of the 25 and Up (25Up) study was to assess a wide range of psychological and behavioral risk factors behind mental illness in a large cohort of Australian twins and their non-twin siblings. Participants had already been studied longitudinally from the age of 12 and most recently in the 19Up study (mean age = 26.1 years, SD = 4.1, range = 20–39). This subsequent wave follows up these twins several years later in life (mean age = 29.7 years, SD = 2.2, range = 22–44). The resulting data set enables additional detailed investigations of genetic pathways underlying psychiatric illnesses in the Brisbane Longitudinal Twin Study (BLTS). Data were collected between 2016 and 2018 from 2540 twins and their non-twin siblings (59% female, including 341 monozygotic complete twin-pairs, 415 dizygotic complete pairs and 1028 non-twin siblings and singletons). Participants were from South-East Queensland, Australia, and the sample was of predominantly European ancestry. The 25Up study collected information on 20 different mental disorders, including depression, anxiety, substance use, psychosis, bipolar and attention-deficit hyper-activity disorder, as well as general demographic information such as occupation, education level, number of children, self-perceived IQ and household environment. In this article, we describe the prevalence, comorbidities and age of onset for all 20 examined disorders. The 25Up study also assessed general and physical health, including physical activity, sleep patterns, eating behaviors, baldness, acne, migraines and allergies, as well as psychosocial items such as suicidality, perceived stress, loneliness, aggression, sleep–wake cycle, sexual identity and preferences, technology and internet use, traumatic life events, gambling and cyberbullying. In addition, 25Up assessed female health traits such as morning sickness, breastfeeding and endometriosis. Furthermore, given that the 25Up study is an extension of previous BLTS studies, 86% of participants have already been genotyped. This rich resource will enable the assessment of epidemiological risk factors, as well as the heritability and genetic correlations of mental conditions.