Identification of novel rare variants for anxiety : an exome-wide association study in the UK Biobank
Copyright © 2023 Elsevier Inc. All rights reserved..
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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 (PBonferroni adjusted = 3.40 × 10-6), MOAP1(PBonferroni adjusted = 4.35 × 10-4), CCDC110 (PBonferroni adjusted = 5.83 × 10-4). Single-variant test detected 9 rare variants, such as rs35726701(RNF123)(PBonferroni adjusted = 3.16 × 10-10) and rs16942615(CAMTA2) (PBonferroni adjusted = 4.04 × 10-4). Notably, RNF123, CCDC110, DNAH2, and CSKMT gene were identified in both tests.
CONCLUSIONS: Our study identified novel candidate genes for anxiety in protein-coding regions, revealing the contribution of rare variants to anxiety.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:130 |
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| Enthalten in: |
Progress in neuro-psychopharmacology & biological psychiatry - 130(2024) vom: 02. Jan., Seite 110928 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Pan, Chuyu [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 15.01.2024 Date Revised 15.01.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.pnpbp.2023.110928 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 245 | 1 | 0 | |a Identification of novel rare variants for anxiety |b an exome-wide association study in the UK Biobank |
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| 500 | |a Date Completed 15.01.2024 | ||
| 500 | |a Date Revised 15.01.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 Elsevier Inc. All rights reserved. | ||
| 520 | |a BACKGROUND: 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 | ||
| 520 | |a METHODS: 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 | ||
| 520 | |a RESULTS: 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 (PBonferroni adjusted = 3.40 × 10-6), MOAP1(PBonferroni adjusted = 4.35 × 10-4), CCDC110 (PBonferroni adjusted = 5.83 × 10-4). Single-variant test detected 9 rare variants, such as rs35726701(RNF123)(PBonferroni adjusted = 3.16 × 10-10) and rs16942615(CAMTA2) (PBonferroni adjusted = 4.04 × 10-4). Notably, RNF123, CCDC110, DNAH2, and CSKMT gene were identified in both tests | ||
| 520 | |a CONCLUSIONS: Our study identified novel candidate genes for anxiety in protein-coding regions, revealing the contribution of rare variants to anxiety | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a 7-item generalized anxiety disorder scale | |
| 650 | 4 | |a Anxiety | |
| 650 | 4 | |a Exome-wide association study | |
| 650 | 4 | |a Protein-coding | |
| 650 | 4 | |a Rare variants | |
| 650 | 7 | |a MOAP1 protein, human |2 NLM | |
| 650 | 7 | |a Adaptor Proteins, Signal Transducing |2 NLM | |
| 650 | 7 | |a Apoptosis Regulatory Proteins |2 NLM | |
| 650 | 7 | |a CAMTA2 protein, human |2 NLM | |
| 650 | 7 | |a Calcium-Binding Proteins |2 NLM | |
| 650 | 7 | |a Trans-Activators |2 NLM | |
| 700 | 1 | |a Cheng, Shiqiang |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Yujing |e verfasserin |4 aut | |
| 700 | 1 | |a Meng, Peilin |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Xuena |e verfasserin |4 aut | |
| 700 | 1 | |a Li, Chun'e |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Jingxi |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Zhen |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Huijie |e verfasserin |4 aut | |
| 700 | 1 | |a Cheng, Bolun |e verfasserin |4 aut | |
| 700 | 1 | |a Wen, Yan |e verfasserin |4 aut | |
| 700 | 1 | |a Jia, Yumeng |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Feng |e verfasserin |4 aut | |
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