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Pathway analysis of genome-wide association datasets of personality traits
- Pathway analysis of genome-wide association datasets of personality traits
- Kim, H. -N.; Kim, B. -H.; Cho, J.; Ryu, S.; Shin, H.; Sung, J.; Shin, C.; Cho, N. H.; Sung, Y. A.; Choi, B. -O.; Kim, H. -L.
- Ewha Authors
- 김형래; 성연아; 김한나
- SCOPUS Author ID
- 김형래; 성연아; 김한나
- Issue Date
- Journal Title
- GENES BRAIN AND BEHAVIOR
- GENES BRAIN AND BEHAVIOR vol. 14, no. 4, pp. 345 - 356
- Axon guidance; behavior; Big Five personality; CAM; five-factor model; genome-wide association study; GWA study; pathway analysis; personality; potassium channel
- SCIE; SCOPUS
- Document Type
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- Although several genome-wide association (GWA) studies of human personality have been recently published, genetic variants that are highly associated with certain personality traits remain unknown, due to difficulty reproducing results. To further investigate these genetic variants, we assessed biological pathways using GWA datasets. Pathway analysis using GWA data was performed on 1089 Korean women whose personality traits were measured with the Revised NEO Personality Inventory for the 5-factor model of personality. A total of 1042 pathways containing 8297 genes were included in our study. Of these, 14 pathways were highly enriched with association signals that were validated in 1490 independent samples. These pathways include association of: Neuroticism with axon guidance [L1 cell adhesion molecule (L1CAM) interactions]; Extraversion with neuronal system and voltage-gated potassium channels; Agreeableness with L1CAM interaction, neurotransmitter receptor binding and downstream transmission in postsynaptic cells; and Conscientiousness with the interferon-gamma and platelet-derived growth factor receptor beta polypeptide pathways. Several genes that contribute to top-ranked pathways in this study were previously identified in GWA studies or by pathway analysis in schizophrenia or other neuropsychiatric disorders. Here we report the first pathway analysis of all five personality traits. Importantly, our analysis identified novel pathways that contribute to understanding the etiology of personality traits.
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