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Rare variants are believed to play a substantial role in the genetic architecture of mental disorders, particularly in coding regions. However, limited evidence supports the impact of rare variants on anxiety. Using whole-exome sequencing data from 200,643 participants in the UK Biobank, we investigated the contribution of rare variants to anxiety. Firstly, we computed genetic risk score (GRS) of anxiety utilizing genotype data and summary data from a genome-wide association study (GWAS) on anxiety disorder. Subsequently, we identified individuals within the lowest 50% GRS, a subgroup more likely to carry pathogenic rare variants. Within this subgroup, we classified individuals with the highest 10% 7-item Generalized Anxiety Disorder scale (GAD-7) score as cases (N = 1869), and those with the lowest 10% GAD-7 score were designated as controls (N = 1869). Finally, we conducted gene-based burden tests and single-variant association analyses to assess the relationship between rare variants and anxiety. Totally, 47,800 variants with MAF ≤0.01 were annotated as non-benign coding variants, consisting of 42,698 nonsynonymous SNVs, 489 nonframeshift substitution, 236 frameshift substitution, 617 stop-gain and 40 stop-loss variants. After variation aggregation, 5066 genes were included in gene-based association analysis. Totally, 11 candidate genes were detected in burden test, such as RNF123 (P Bonferroni adjusted = 3.40 × 10−6), MOAP1 (P Bonferroni adjusted = 4.35 × 10−4), CCDC110 (P Bonferroni adjusted = 5.83 × 10−4). Single-variant test detected 9 rare variants, such as rs35726701(RNF123)(P Bonferroni adjusted = 3.16 × 10−10) and rs16942615(CAMTA2) (P Bonferroni adjusted = 4.04 × 10−4). Notably, RNF123 , CCDC110 , DNAH2 , and CSKMT gene were identified in both tests. Our study identified novel candidate genes for anxiety in protein-coding regions, revealing the contribution of rare variants to anxiety. • We investigated the rare variants for anxiety among 3738 European individuals with lower anxiety GWAS-derived polygenic risk. • Gene-based burden test revealed 11 candidate genes for anxiety, such as RNF123 , DNAH2 and CCDC110. • Single variant association analysis detected 9 rare variants for anxiety, such as rs35726701(RNF123) and rs16942615(CAMTA2). [ABSTRACT FROM AUTHOR]

Previous genetic studies of anxiety and depression were mostly based on independent phenotypes. This study aims to investigate the shared and specific genetic structure between anxiety and depression. To identify the underlying factors of Generalized Anxiety Disorder-7 (GAD-7), Patient Health Questionnaire-9 (PHQ-9), and their combined scale (joint scale), we employed exploratory factor analysis (EFA) using the eigenvalue of parallel analysis. Subsequently, we conducted a genome-wide association study (GWAS) for these factors. In addition, we utilized LD Score Regression (LDSC) to determine the genetic correlations between the identified factors and four common mental disorders, three sleep phenotypes, and other traits that have been previously linked to anxiety and depression. The EFA uncovered two factors for the GAD-7 scale, namely nervousness and disturbance, two factors for the PHQ-9 scale, namely negative affect and sleep/appetite disturbance, and four factors for the joint scale, specifically nervousness, anhedonia, sleep/appetite disturbance, and fidget. We identified two genome-wide significant genomic loci, with overlap across GAD-7 factor 1 and joint scale factor 1: rs148579586 (P GAD-7 = 1.365 × 10−09, P Joint scale = 1.434 × 10–09) and rs201074060 (P GAD-7 = 3.672 × 10−09, P Joint scale = 3.824 × 10−09). Genetic correlations in factors ranged from 0.722 to 1.000 (all p < 1.786 × 10−03) with 27 of 28 correlations being significantly smaller than one. The genetic correlations with external phenotypes showed small variation across the eight factors. Unidimensional structures can provide more precise scores, which can aid in identifying the shared and specific genetic associations between anxiety and depression. This is a crucial step in characterizing the genetic structure of these conditions and their co-occurrence. • We used EFA to identify potential factors for GAD-7, PHQ-9 and the joint scale. • we found common and specific genetic structures between anxiety and depression. • Single-dimensional structures can build more precise fractions. [ABSTRACT FROM AUTHOR]