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Functional and molecular characterization of suicidality factors using phenotypic and genome-wide data

Genome-wide association studies (GWAS) of suicidal thoughts and behaviors support the existence of genetic contributions. Continuous measures of psychiatric disorder symptom severity can sometimes model polygenic risk better than binarized definitions. We compared two severity measures of suicidal t...

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Autores principales: Quintero Reis, Andrea, Newton, Brendan A., Kessler, Ronald, Polimanti, Renato, Wendt, Frank R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10005939/
https://www.ncbi.nlm.nih.gov/pubmed/36604601
http://dx.doi.org/10.1038/s41380-022-01929-5
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author Quintero Reis, Andrea
Newton, Brendan A.
Kessler, Ronald
Polimanti, Renato
Wendt, Frank R.
author_facet Quintero Reis, Andrea
Newton, Brendan A.
Kessler, Ronald
Polimanti, Renato
Wendt, Frank R.
author_sort Quintero Reis, Andrea
collection PubMed
description Genome-wide association studies (GWAS) of suicidal thoughts and behaviors support the existence of genetic contributions. Continuous measures of psychiatric disorder symptom severity can sometimes model polygenic risk better than binarized definitions. We compared two severity measures of suicidal thoughts and behaviors at the molecular and functional levels using genome-wide data. We used summary association data from GWAS of four traits analyzed in 122,935 individuals of European ancestry: thought life was not worth living (TLNWL), thoughts of self-harm, actual self-harm, and attempted suicide. A new trait for suicidal thoughts and behaviors was constructed first, phenotypically, by aggregating the previous four traits (termed “suicidality”) and second, genetically, by using genomic structural equation modeling (gSEM; termed S-factor). Suicidality and S-factor were compared using SNP-heritability (h(2)) estimates, genetic correlation (r(g)), partitioned h(2), effect size distribution, transcriptomic correlations (ρ(GE)) in the brain, and cross-population polygenic scoring (PGS). The S-factor had good model fit (χ(2) = 0.21, AIC = 16.21, CFI = 1.00, SRMR = 0.024). Suicidality (h(2) = 7.6%) had higher h(2) than the S-factor (h(2) = 2.54, P(diff) = 4.78 × 10(−13)). Although the S-factor had a larger number of non-null susceptibility loci (π(c) = 0.010), these loci had small effect sizes compared to those influencing suicidality (π(c) = 0.005, P(diff) = 0.045). The h(2) of both traits was enriched for conserved biological pathways. The r(g) and ρ(GE) support highly overlapping genetic and transcriptomic features between suicidality and the S-factor. PGS using European-ancestry SNP effect sizes strongly associated with TLNWL in Admixed Americans: Nagelkerke’s R(2) = 8.56%, P = 0.009 (PGS(suicidality)) and Nagelkerke’s R(2) = 7.48%, P = 0.045 (PGS(S-factor)). An aggregate suicidality phenotype was statistically more heritable than the S-factor across all analyses and may be more informative for future genetic study designs interested in common genetic factors among different suicide related phenotypes.
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spelling pubmed-100059392023-03-12 Functional and molecular characterization of suicidality factors using phenotypic and genome-wide data Quintero Reis, Andrea Newton, Brendan A. Kessler, Ronald Polimanti, Renato Wendt, Frank R. Mol Psychiatry Article Genome-wide association studies (GWAS) of suicidal thoughts and behaviors support the existence of genetic contributions. Continuous measures of psychiatric disorder symptom severity can sometimes model polygenic risk better than binarized definitions. We compared two severity measures of suicidal thoughts and behaviors at the molecular and functional levels using genome-wide data. We used summary association data from GWAS of four traits analyzed in 122,935 individuals of European ancestry: thought life was not worth living (TLNWL), thoughts of self-harm, actual self-harm, and attempted suicide. A new trait for suicidal thoughts and behaviors was constructed first, phenotypically, by aggregating the previous four traits (termed “suicidality”) and second, genetically, by using genomic structural equation modeling (gSEM; termed S-factor). Suicidality and S-factor were compared using SNP-heritability (h(2)) estimates, genetic correlation (r(g)), partitioned h(2), effect size distribution, transcriptomic correlations (ρ(GE)) in the brain, and cross-population polygenic scoring (PGS). The S-factor had good model fit (χ(2) = 0.21, AIC = 16.21, CFI = 1.00, SRMR = 0.024). Suicidality (h(2) = 7.6%) had higher h(2) than the S-factor (h(2) = 2.54, P(diff) = 4.78 × 10(−13)). Although the S-factor had a larger number of non-null susceptibility loci (π(c) = 0.010), these loci had small effect sizes compared to those influencing suicidality (π(c) = 0.005, P(diff) = 0.045). The h(2) of both traits was enriched for conserved biological pathways. The r(g) and ρ(GE) support highly overlapping genetic and transcriptomic features between suicidality and the S-factor. PGS using European-ancestry SNP effect sizes strongly associated with TLNWL in Admixed Americans: Nagelkerke’s R(2) = 8.56%, P = 0.009 (PGS(suicidality)) and Nagelkerke’s R(2) = 7.48%, P = 0.045 (PGS(S-factor)). An aggregate suicidality phenotype was statistically more heritable than the S-factor across all analyses and may be more informative for future genetic study designs interested in common genetic factors among different suicide related phenotypes. Nature Publishing Group UK 2023-01-06 2023 /pmc/articles/PMC10005939/ /pubmed/36604601 http://dx.doi.org/10.1038/s41380-022-01929-5 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Quintero Reis, Andrea
Newton, Brendan A.
Kessler, Ronald
Polimanti, Renato
Wendt, Frank R.
Functional and molecular characterization of suicidality factors using phenotypic and genome-wide data
title Functional and molecular characterization of suicidality factors using phenotypic and genome-wide data
title_full Functional and molecular characterization of suicidality factors using phenotypic and genome-wide data
title_fullStr Functional and molecular characterization of suicidality factors using phenotypic and genome-wide data
title_full_unstemmed Functional and molecular characterization of suicidality factors using phenotypic and genome-wide data
title_short Functional and molecular characterization of suicidality factors using phenotypic and genome-wide data
title_sort functional and molecular characterization of suicidality factors using phenotypic and genome-wide data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10005939/
https://www.ncbi.nlm.nih.gov/pubmed/36604601
http://dx.doi.org/10.1038/s41380-022-01929-5
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