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Genome-wide association studies (GWAS) of psychological traits are generally conducted on (dichotomized) sums of items or symptoms (e.g., case-control status), and not on the individual items or symptoms themselves. We conduct large-scale GWAS on 12 neuroticism items and observe notable and replicable variation in genetic signal between items. Within samples, genetic correlations among the items range between 0.38 and 0.91 (mean r g  = .63), indicating genetic heterogeneity in the full item set. Meta-analyzing the two samples, we identify 255 genome-wide significant independent genomic regions, of which 138 are item-specific. Genetic analyses and genetic correlations with 33 external traits support genetic differences between the items. Hierarchical clustering analysis identifies two genetically homogeneous item clusters denoted depressed affect and worry. We conclude that the items used to measure neuroticism are genetically heterogeneous, and that biological understanding can be gained by studying them in genetically more homogeneous clusters. Neuroticism can be assessed as a composite score of individual items. Here, Nagel et al. perform genetic association studies for 12 neuroticism items and the sum-score and demonstrate genetic heterogeneity at the item-level.

The widespread comorbidity among psychiatric disorders demonstrated in epidemiological studies 1 – 5 is mirrored by non-zero, positive genetic correlations from large-scale genetic studies 6 – 10 . To identify shared biological processes underpinning this observed phenotypic and genetic covariance and enhance molecular characterization of general psychiatric disorder liability 11 – 13 , we used several strategies aimed at uncovering pleiotropic, that is, cross-trait-associated, single-nucleotide polymorphisms (SNPs), genes and biological pathways. We conducted cross-trait meta-analysis on 12 psychiatric disorders to identify pleiotropic SNPs. The meta-analytic signal was driven by schizophrenia, hampering interpretation and joint biological characterization of the cross-trait meta-analytic signal. Subsequent pairwise comparisons of psychiatric disorders identified substantial pleiotropic overlap, but mainly among pairs of psychiatric disorders, and mainly at less stringent P -value thresholds. Only annotations related to evolutionarily conserved genomic regions were significant for multiple (9 out of 12) psychiatric disorders. Overall, identification of shared biological mechanisms remains challenging due to variation in power and genetic architecture between psychiatric disorders. Cross-trait meta-analysis on 12 psychiatric disorders identifies the genetic overlap between pairs of disorders and highlights the challenges of cross-trait genetic research.

The phenotypic correlation between human intelligence and brain volume (BV) is considerable ( r  ≈ 0.40), and has been shown to be due to shared genetic factors. To further examine specific genetic factors driving this correlation, we present genomic analyses of the genetic overlap between intelligence and BV using genome-wide association study (GWAS) results. First, we conduct a large BV GWAS meta-analysis (N = 47,316 individuals), followed by functional annotation and gene-mapping. We identify 18 genomic loci (14 not previously associated), implicating 343 genes (270 not previously associated) and 18 biological pathways for BV. Second, we use an existing GWAS for intelligence (N = 269,867 individuals), and estimate the genetic correlation ( r g ) between BV and intelligence to be 0.24. We show that the r g is partly attributable to physical overlap of GWAS hits in 5 genomic loci. We identify 92 shared genes between BV and intelligence, which are mainly involved in signaling pathways regulating cell growth. Out of these 92, we prioritize 32 that are most likely to have functional impact. These results provide information on the genetics of BV and provide biological insight into BV’s shared genetic etiology with intelligence. Brain volume and intelligence have been previously found to have shared genetic etiology, but the specific common genetic signals have not been identified. Here, the authors perform a genome-wide association study on brain volume, finding common genetic loci driving brain volume and intelligence.

Cerebellar volume is highly heritable and associated with neurodevelopmental and neurodegenerative disorders. Understanding the genetic architecture of cerebellar volume may improve our insight into these disorders. This study aims to investigate the convergence of cerebellar volume genetic associations in close detail. A genome-wide associations study for cerebellar volume was performed in a discovery sample of 27,486 individuals from UK Biobank, resulting in 30 genome-wide significant loci and a SNP heritability of 39.82%. We pinpoint the likely causal variants and those that have effects on amino acid sequence or cerebellar gene-expression. Additionally, 85 genome-wide significant genes were detected and tested for convergence onto biological pathways, cerebellar cell types, human evolutionary genes or developmental stages. Local genetic correlations between cerebellar volume and neurodevelopmental and neurodegenerative disorders reveal shared loci with Parkinson’s disease, Alzheimer’s disease and schizophrenia. These results provide insights into the heritable mechanisms that contribute to developing a brain structure important for cognitive functioning and mental health. A genome-wide association study on MRI cerebellar volume in the UK Biobank cohort identifies 30 loci with genome-wide significance that might be relevant to brain structure and cognitive function.

Mental health problems are highly prevalent in modern-day society. Despite several decades of intensive research aimed at identifying the underlying biological mechanisms, and potential drug targets, pharmacological treatments still have limited success. Since all traits are at least partially influenced by our genetic makeup, using genetic information to increase our understanding of the biological mechanisms underlying mental health problems might eventually benefit patients. Genome-wide association studies (GWAS) provide an exploratory way to identify genetic variants throughout the genome that are, statistically, associated to a trait of interest. The explosion of GWAS studies since 2005 (https://www.ebi.ac.uk/gwas/diagram) has drastically increased our knowledge of the biology of diseases and identified thousands of variants involved in a wide variety of (disease) traits. Yet for many complex traits, like psychiatric disorders, the identified genetic variants explain only a fraction of the variance in the trait. We argue that this may, in part, be the result of the way in which neuropsychiatric traits are operationalized in genetic studies. Typically, participants are classified as cases (i.e., people that suffer from a given psychiatric disorder) or as controls (i.e., not suffering from that particular disorder). However, people suffering from the same disorder may exhibit different sets of symptoms that may, in turn, be influenced by different genetic variants. In other words, the manner in which phenotypes are operationalized will have consequences for the success of genetic analyses. Therefore, in order to properly study the genetic basis of complex behavior, it is vital to think about the exact nature of the phenotypes used in the analysis, and the way they are operationalized. This thesis uses large-scale genetic data and state-of-the-art methods to study the merits of more detailed phenotyping in uncovering the genetics of complex neuropsychiatric traits

Genetic pleiotropy is abundant across spatially distributed brain characteristics derived from one neuroimaging modality (e.g. structural, functional or diffusion MRI). A better understanding of pleiotropy across modalities could inform us on the integration of brain function, micro- and macrostructure. Here we show extensive genetic overlap across neuroimaging modalities at a locus and gene level in the UK Biobank (N=34,029) and ABCD Study (N=8,607). When jointly analysing phenotypes derived from structural, functional and diffusion MRI with the Multivariate Omnibus Statistical Test (MOSTest), we boost the discovery of loci and genes beyond previously identified effects for each modality individually. Cross-modality genes are involved in fundamental biological processes and predominantly expressed during prenatal brain development. We additionally boost prediction of psychiatric disorders by conditioning independent GWAS on our multimodal multivariate GWAS. These findings shed light on the shared genetic mechanisms underlying variation in brain morphology, functional connectivity, and tissue composition.Competing Interest StatementE.P.T., A.A.S., D.v.d.M, N.P., G.H., D.R., O.F., C.C.F., M.N., T.N., M.B., S.D., L.T.W., M.P.v.d.H., D.P. and T.K. declare no conflicts of interest. Dr. Andreassen has received speaker's honorarium from Lundbeck and Janssen, and is a consultant to coretechs.ai. Dr. Dale is a Founder of and holds equity in CorTechs Labs, Inc, and serves on its Scientific Advisory Board. He is a member of the Scientific Advisory Board of Human Longevity, Inc. and receives funding through research agreements with General Electric Healthcare and Medtronic, Inc. The terms of these arrangements have been reviewed and approved by UCSD in accordance with its conflict of interest policies.Footnotes* Revision of methods to assess polygenic score improvement. Revision of main text on condFDR.

The functional connectivity and dynamics of resting-state networks (RSN-FC) are vital for cognitive functioning. RSN-FC is heritable and partially translates to the anatomical architecture of white matter, but the genetic component of structural connections of RSNs (RSN-SC) and their potential genetic overlap with RSN-FC remains unknown. Here we perform genome-wide association studies (Ndiscovery=24,336; Nreplication=3,412) and in silico annotation on RSN-SC and RSN-FC. We identify the first genes for visual network-SC, that are involved in axon guidance and synaptic functioning and show that genetic variation in RSN-FC impacts biological processes related to brain disorders that have previously been associated with FC alterations in those same RSNs. Correlations of the genetic components of RSNs are mostly observed within the functional domain, whereas less overlap is observed within the structural domain and between the functional and structural domains. This study advances the understanding of the complex functional organization of the brain and its structural underpinnings from a genetics viewpoint. Competing Interest Statement The authors have declared no competing interest.

The phenotypic correlation between human intelligence and brain volume (BV) is considerable (r≈0.40), and has been shown to be due to shared genetic factors. To further examine specific genetic factors driving this correlation, we present genomic analyses of the genetic overlap between intelligence and BV using genome-wide association study (GWAS) results. First, we conducted the largest BV GWAS meta-analysis to date (N=54,407 individuals), followed by functional annotation and gene-mapping. We identified 35 genomic loci (27 novel), implicating 362 genes (346 novel) and 23 biological pathways for BV. Second, we used an existing GWAS for intelligence (N=269,867 individuals), and estimated the genetic correlation (rg) between BV and intelligence to be 0.23. We show that the rg is driven by physical overlap of GWAS hits in 5 genomic loci. We identified 67 shared genes between BV and intelligence, which are mainly involved in important signaling pathways regulating cell growth. Out of these 67 we prioritized 32 that are most likely to have functional impact. These results provide new information on the genetics of BV and provide biological insight into BV's shared genetic etiology with intelligence.

Neuroticism is an important risk factor for psychiatric traits including depression, anxiety, and schizophrenia. Previous genome-wide association studies (GWAS) reported 16 genomic loci. Here we report the largest neuroticism GWAS meta-analysis to date (N=449,484), and identify 136 independent genome-wide significant loci (124 novel), implicating 599 genes. Extensive functional follow-up analyses show enrichment in several brain regions and involvement of specific cell-types, including dopaminergic neuroblasts (P=3E-8), medium spiny neurons (P=4E-8) and serotonergic neurons (P=1E-7). Gene-set analyses implicate three specific pathways: neurogenesis (P=4.4E-9), behavioural response to cocaine processes (P=1.84E-7), and axon part (P=5.26E-8). We show that neuroticism's genetic signal partly originates in two genetically distinguishable subclusters (depressed affect and worry, the former being genetically strongly related to depression, rg=0.84), suggesting distinct causal mechanisms for subtypes of individuals. These results vastly enhance our neurobiological understanding of neuroticism, and provide specific leads for functional follow-up experiments.