Literature DB >> 29573219

Predicting loneliness with polygenic scores of social, psychological and psychiatric traits.

A Abdellaoui1,2, M G Nivard1, J-J Hottenga1, I Fedko1, K J H Verweij2, B M L Baselmans1,3, E A Ehli4, G E Davies4, M Bartels1,3,5, D I Boomsma1,3,5, J T Cacioppo6.   

Abstract

Loneliness is a heritable trait that accompanies multiple disorders. The association between loneliness and mental health indices may partly be due to inherited biological factors. We constructed polygenic scores for 27 traits related to behavior, cognition and mental health and tested their prediction for self-reported loneliness in a population-based sample of 8798 Dutch individuals. Polygenic scores for major depressive disorder (MDD), schizophrenia and bipolar disorder were significantly associated with loneliness. Of the Big Five personality dimensions, polygenic scores for neuroticism and conscientiousness also significantly predicted loneliness, as did the polygenic scores for subjective well-being, tiredness and self-rated health. When including all polygenic scores simultaneously into one model, only 2 major depression polygenic scores remained as significant predictors of loneliness. When controlling only for these 2 MDD polygenic scores, only neuroticism and schizophrenia remain significant. The total variation explained by all polygenic scores collectively was 1.7%. The association between the propensity to feel lonely and the susceptibility to psychiatric disorders thus pointed to a shared genetic etiology. The predictive power of polygenic scores will increase as the power of the genome-wide association studies on which they are based increases and may lead to clinically useful polygenic scores that can inform on the genetic predisposition to loneliness and mental health.
© 2018 John Wiley & Sons Ltd and International Behavioural and Neural Genetics Society.

Entities:  

Keywords:  genetic correlation; genetic prediction; loneliness; major depressive disorder; polygenic scores

Mesh:

Year:  2018        PMID: 29573219      PMCID: PMC6464630          DOI: 10.1111/gbb.12472

Source DB:  PubMed          Journal:  Genes Brain Behav        ISSN: 1601-183X            Impact factor:   3.449


  9 in total

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Journal:  Neuropsychopharmacology       Date:  2019-07-08       Impact factor: 7.853

4.  Polygenic risk for mental disorder reveals distinct association profiles across social behaviour in the general population.

Authors:  Fenja Schlag; Andrea G Allegrini; Jan Buitelaar; Ellen Verhoef; Marjolein van Donkelaar; Robert Plomin; Kaili Rimfeld; Simon E Fisher; Beate St Pourcain
Journal:  Mol Psychiatry       Date:  2022-02-28       Impact factor: 13.437

5.  A Genetic Investigation of the Well-Being Spectrum.

Authors:  B M L Baselmans; M P van de Weijer; A Abdellaoui; J M Vink; J J Hottenga; G Willemsen; M G Nivard; E J C de Geus; D I Boomsma; M Bartels
Journal:  Behav Genet       Date:  2019-02-27       Impact factor: 2.805

6.  Connectome-based individualized prediction of loneliness.

Authors:  Chunliang Feng; Li Wang; Ting Li; Pengfei Xu
Journal:  Soc Cogn Affect Neurosci       Date:  2019-05-17       Impact factor: 3.436

7.  It is time to get real when trying to predict educational performance.

Authors:  Cecile Janssens
Journal:  Elife       Date:  2020-03-13       Impact factor: 8.140

8.  Phenome-wide investigation of health outcomes associated with genetic predisposition to loneliness.

Authors:  Abdel Abdellaoui; Sandra Sanchez-Roige; Julia Sealock; Jorien L Treur; Jessica Dennis; Pierre Fontanillas; Sarah Elson; Michel G Nivard; Hill Fung Ip; Matthijs van der Zee; Bart M L Baselmans; Jouke Jan Hottenga; Gonneke Willemsen; Miriam Mosing; Yi Lu; Nancy L Pedersen; Damiaan Denys; Najaf Amin; Cornelia M van Duijn; Ingrid Szilagyi; Henning Tiemeier; Alexander Neumann; Karin J H Verweij; Stephanie Cacioppo; John T Cacioppo; Lea K Davis; Abraham A Palmer; Dorret I Boomsma
Journal:  Hum Mol Genet       Date:  2019-11-15       Impact factor: 6.150

9.  Effects of the Openness to Experience Polygenic Score on Cortical Thickness and Functional Connectivity.

Authors:  Zhiting Ren; Cheng Liu; Jie Meng; Qiang Liu; Liang Shi; Xinran Wu; Li Song; Jiang Qiu
Journal:  Front Neurosci       Date:  2021-01-11       Impact factor: 4.677

  9 in total

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