Schizophrenia is a severe psychiatric disorder, associated with a reduction in life expectancy of 15-20 years. Available treatments are at least partially effective in most affected individuals, and personal resources such as resilience (successful adaptation despite adversity) and coping abilities (strategies used to deal with stressful or threatening situations), are important determinants of disease outcomes and long-term sustained recovery. Published findings support the existence of a genetic background underlying resilience and coping, with variable heritability estimates. However, genome-wide analyses concerning the genetic determinants of these personal resources, especially in the context of schizophrenia, are lacking. Here, we performed a genome-wide association study coupled with accessory analyses to investigate potential genetic determinants of resilience, coping and self-esteem in 490 schizophrenia patients. Results revealed a complex genetic background partly overlapping with that of neuroticism, worry and schizophrenia itself and support the importance of social aspects in shapingthese psychological constructs. Hippocampal neurogenesis and lipid metabolism appear to be potentially relevant biological underpinnings, and specific miRNAs such as miR-124 and miR-137 may warrant further studies as potential biomarkers. In conclusion, this study represents an important first step in the identification of genetic and biological correlates shaping resilience, coping resources and self-esteem in schizophrenia.

BACKGROUND: Relatives of ADHD probands are known to be at increased risk of schizophrenia and bipolar disorder, suggesting shared genetic factors. In this study, we aim to identify shared common risk variants (i.e., Single-Nucleotide Polymorphisms, SNPs) between ADHD and schizophrenia, and between ADHD and bipolar disorder.
METHODS: With the summary data from three GWAS, one on ADHD (20,183 cases with ADHD and 35,191 controls), another on schizophrenia (76,755 cases with schizophrenia and 243,649 controls) and another on bipolar disorder (41,917 cases with bipolar disorder and 371,549 controls), we used colocalization analysis to identify SNPs shared by ADHD and schizophrenia, and SNPs shared by ADHD and bipolar disorder. Functional genomic analyses were then conducted on these two sets of shared common genetic variants.
RESULTS: We found that three of the 12 SNPs associated with ADHD colocalized with schizophrenia SNPs and one of the 12 SNPs associated with ADHD colocalized with bipolar disorder. Only 0.4% of the SNPs associated with schizophrenia (2 out of 431) and 2.3% of the SNPs associated with bipolar disorder (2 out of 86), colocalized with ADHD SNPs. Some genes mapped to these shared genetic variants (SCN2A and UNC5D) are involved in the development of the nervous system.
CONCLUSIONS: Using colocalization analysis, the present study uncovers shared genetic variants associated with ADHD and schizophrenia as well as ADHD and bipolar disorder, and may at least partially explain the increased risk of schizophrenia and bipolar disorder in relatives of ADHD probands.

BACKGROUND: Constitutional delay of puberty (CDP) is highly heritable, but the genetic basis for CDP is largely unknown. Idiopathic hypogonadotropic hypogonadism (IHH) can be caused by rare genetic variants, but in about half of cases, no rare-variant cause is found.
OBJECTIVE: To determine whether common genetic variants that influence pubertal timing contribute to CDP and IHH.
METHODS: Case-control study.
METHODS: 80 individuals with CDP; 301 with normosmic IHH, and 348 with Kallmann syndrome; control genotyping data from unrelated studies.
METHODS: Polygenic scores (PGS) based on genome-wide association studies for timing of male pubertal hallmarks and age at menarche (AAM).
RESULTS: The CDP cohort had higher PGS for male pubertal hallmarks and for AAM compared to controls (for male hallmarks, Cohen\'s d = 0.85, p = 1 × 10-16; for AAM, d = 0.67, p = 1 × 10-10). The normosmic IHH cohort also had higher PGS for male hallmarks compared to controls, but the difference was smaller (male hallmarks d = 0.20, p = 0.003; AAM d = 0.10, p = 0.055). No differences were seen for the KS cohort compared to controls (male hallmarks d = 0.04, p = 0.45; AAM d = -0.03, p = 0.86).
CONCLUSIONS: Common genetic variants that influence pubertal timing in the general population contribute strongly to the genetics of CDP, weakly to normosmic IHH, and potentially not at all to KS. These findings demonstrate that the common-variant genetics of CDP and normosmic IHH are largely but not entirely distinct.

Creativity is known to be heritable and exhibits familial aggregation with psychiatric disorders; however, the complex nature of their relationship has not been well-established. In the present study, we demonstrate that using an expanded and validated machine learning (ML)-based phenotyping of occupational creativity (OC) can allow us to further understand the trait of creativity, which was previously difficult to define and study. We conducted the largest genome-wide association study (GWAS) on OC with 241,736 participants from the UK Biobank and identified 25 lead variants that have not yet been reported and three candidate causal genes that were previously associated with educational attainment and psychiatric disorders. We found extensive genetic overlap between OC and psychiatric disorders with mixed effect direction through various post-GWAS analyses, including the bivariate causal mixture model. In addition, we discovered a strongly genetic correlation between our original GWAS and the GWAS adjusted for education years (rg = 0.95). Our GWAS analysis via ML-based phenotyping contributes to the understanding of the genetic architecture of creativity, which may inform genetic discovery and genetic prediction in human cognition and psychiatric disorders.

Patients with schizophrenia (SCZ) frequently exhibit an elevated risk of metabolic syndrome (MetS), which may lead to a worse clinical outcome. Even though these two phenotypes are genetically linked, little is known about their shared genetic determinants. Here, we investigated whether SCZ and MetS share genetic risk factors. To examine the genetic overlap between the two disorders, we applied a comprehensive genetic overlap analysis by integrating GWAS data for SCZ (n = 320,404) and MetS (n = 291,107) at the genome, genetic variants, and gene levels. At the genome level, we observed polygenic overlap between SCZ and MetS by utilizing LDSC (rg=-0.09, P = 1 × 10-4) and GNOVA (rho=-0.04, P = 1.39 × 10-8) analysis. At the SNP level, we performed conjunctional FDR (conjFDR) analysis to identify genetic variants simultaneously associated with two phenotypes. Based on conjFDR < 0.05, we identified 22 loci shared between SCZ and MetS. At the gene level, we further demonstrated that SCZ- and MetS-inferred gene expression overlapped across 49 GTEx tissues and highlighted the PCCB and KCTD13 genes as putative mediators of the genetic association. Overall, these findings shed novel light on the association between SCZ and MetS, and potentially enhance our knowledge of the high comorbidity and genetic processes that overlap between the two disorders.

Host genetic variability plays a pivotal role in modulating COVID-19 clinical outcomes. Despite the functional relevance of protein-coding regions, rare variants located here are less likely to completely explain the considerable numbers of acutely affected COVID-19 patients worldwide. Using an exome-wide association approach, with individuals of European descent, we sought to identify common coding variants linked with variation in COVID-19 severity. Herein, cohort 1 compared non-hospitalized (controls) and hospitalized (cases) individuals, and in cohort 2, hospitalized subjects requiring respiratory support (cases) were compared to those not requiring it (controls). 229 and 111 variants differed significantly between cases and controls in cohorts 1 and 2, respectively. This included FBXO34, CNTN2, and TMCC2 previously linked with COVID-19 severity using association studies. Overall, we report SNPs in 26 known and 12 novel candidate genes with strong molecular evidence implicating them in the pathophysiology of life-threatening COVID-19 and post-recovery sequelae. Of these few notable known genes include, HLA-DQB1, AHSG, ALOX5AP, MUC5AC, SMPD1, SPG7, SPEG,GAS6, and SERPINA12. These results enhance our understanding of the pathomechanisms underlying the COVID-19 clinical spectrum and may be exploited to prioritize biomarkers for predicting disease severity, as well as to improve treatment strategies in individuals of European ancestry.

OBJECTIVE: To determine the contribution of common and rare genetic variants in age-related macular degeneration (AMD) in a Portuguese population from the Coimbra Eye Study (CES), and the genetic risk score (GRS).
METHODS: Participants underwent ophthalmologic examination and imaging. A centralized reading centre performed AMD staging. Genetic sequencing was carried out with the EYE-RISK assay. Sixty-nine single nucleotide polymorphisms (SNPs) were genotyped and tested for association with AMD. Case-control and progression-to-AMD analyses were performed using logistic regression to assess allelic odds ratio (OR) at a 95% confidence interval (CI) for each variant. GRS was calculated for cases/controls and progressors/non-progressors. Cumulative impact of rare variants was compared between cases/controls using logistic regression.
RESULTS: In case-control analysis (237 cases/640 controls) variants associated with risk of disease were: ARMS2 rs10490924, ARMS2_HTRA1 rs3750846, CFH rs35292876, SLC16A8 rs8135665, TGFBR1 rs1626340. Major risk variants ARMS2/HTRA1 rs3750846, CFH rs570618 and C3 rs2230199 had unexpected lower allele frequency (AF), and the highest risk-conferring variant was a rare variant, CFH rs35292876 (OR, 2.668; p-value = 0.021). In progression-to-AMD analysis (137 progressors/630 non-progressors), variants associated with risk of progression were ARMS2 rs10490924, ARMS2_HTRA1 rs3750846, CFH rs35292876. GRS of cases/controls was 1.124 ± 1.187 and 0.645 ± 1.124 (p-value < 0.001), and of progressors/non-progressors was 1.190 ± 1.178 and 0.669 ± 1.141 (p-value < 0.001). Higher proportion of pathogenic rare CFH variants was observed in cases (OR, 9.661; p-value < 0.001).
CONCLUSIONS: Both common and rare variants were associated with AMD, but a CFH rare variant conferred the highest risk of disease while three major risk variants had a lower-than-expected AF in our population originary from a geographic region with lower prevalence of AMD. GRS was still significantly higher in AMD patients. Damaging CFH rare variants were cumulatively more common in AMD cases.

OBJECTIVE: To examine potential genetic relationships between migraine and the two distinct phenotypes posterior circulation ischemic stroke (PCiS) and anterior circulation ischemic stroke (ACiS), we generated migraine polygenic risk scores (PRSs) and compared these between PCiS and ACiS, and separately vs. non-stroke control subjects.
METHODS: Acute ischemic stroke cases were classified as PCiS or ACiS based on lesion location on diffusion-weighted MRI. Exclusion criteria were lesions in both vascular territories or uncertain territory; supratentorial PCiS with ipsilateral fetal posterior cerebral artery; and cases with atrial fibrillation. We generated migraine PRS for three migraine phenotypes (any migraine; migraine without aura; migraine with aura) using publicly available GWAS data and compared mean PRSs separately for PCiS and ACiS vs. non-stroke control subjects, and between each stroke phenotype.
RESULTS: Our primary analyses included 464 PCiS and 1079 ACiS patients with genetic European ancestry. Compared to non-stroke control subjects (n=15396), PRSs of any migraine were associated with increased risk of PCiS (p=0.01-0.03) and decreased risk of ACiS (p=0.010-0.039). Migraine without aura PRSs were significantly associated with PCiS (p=0.008-0.028), but not with ACiS. When comparing PCiS vs. ACiS directly, migraine PRSs were higher in PCiS vs. ACiS for any migraine (p=0.001-0.010) and migraine without aura (p=0.032-0.048). Migraine with aura PRS did not show a differential association in our analyses.
CONCLUSIONS: Our results suggest a stronger genetic overlap between unspecified migraine and migraine without aura with PCiS compared to ACiS. Possible shared mechanisms include dysregulation of cerebral vessel endothelial function.

Interpretation of the function of non-coding risk loci for neuropsychiatric disorders and brain-relevant traits via gene expression and alternative splicing quantitative trait locus (e/sQTL) analyses is generally performed in bulk post-mortem adult tissue. However, genetic risk loci are enriched in regulatory elements active during neocortical differentiation, and regulatory effects of risk variants may be masked by heterogeneity in bulk tissue. Here, we map e/sQTLs, and allele-specific expression in cultured cells representing two major developmental stages, primary human neural progenitors (n = 85) and their sorted neuronal progeny (n = 74), identifying numerous loci not detected in either bulk developing cortical wall or adult cortex. Using colocalization and genetic imputation via transcriptome-wide association, we uncover cell-type-specific regulatory mechanisms underlying risk for brain-relevant traits that are active during neocortical differentiation. Specifically, we identified a progenitor-specific eQTL for CENPW co-localized with common variant associations for cortical surface area and educational attainment.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are two highly heritable neurodevelopmental disorders. Several lines of evidence point towards the presence of shared genetic factors underlying ASD and ADHD. We conducted genomic analyses of common risk variants (i.e. single nucleotide polymorphisms, SNPs) shared by ASD and ADHD, and those specific to each disorder.
METHODS: With the summary data from two GWAS, one on ASD (N = 46,350) and another on ADHD (N = 55,374) individuals, we used genomic structural equation modelling and colocalization analysis to identify SNPs shared by ASD and ADHD and SNPs specific to each disorder. Functional genomic analyses were then conducted on shared and specific common genetic variants. Finally, we performed a bidirectional Mendelian randomization analysis to test whether the shared genetic risk between ASD and ADHD was interpretable in terms of reciprocal relationships between ASD and ADHD.
RESULTS: We found that 37.5% of the SNPs associated with ASD (at p < 1e-6) colocalized with ADHD SNPs and that 19.6% of the SNPs associated with ADHD colocalized with ASD SNPs. We identified genes mapped to SNPs that are specific to ASD or ADHD and that are shared by ASD and ADHD, including two novel genes INSM1 and PAX1. Our bidirectional Mendelian randomization analyses indicated that the risk of ASD was associated with an increased risk of ADHD and vice versa.
CONCLUSIONS: Using multivariate genomic analyses, the present study uncovers shared and specific genetic variants associated with ASD and ADHD. Further functional investigation of genes mapped to those shared variants may help identify pathophysiological pathways and new targets for treatment.