Psychometric precision in phenotype definition is a useful step in molecular genetic investigation of psychiatric disorders

M. K. Xu (Lead / Corresponding author), D. Gaysina, J. H. Barnett, L. Scoriels, L. N. van de Lagemaat, A. Wong, M. Richards, T. J. Croudace, P. B. Jones, LHA Genetics Group

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Abstract

Affective disorders are highly heritable, but few genetic risk variants have been consistently replicated in molecular genetic association studies. The common method of defining psychiatric phenotypes in molecular genetic research is either a summation of symptom scores or binary threshold score representing the risk of diagnosis. Psychometric latent variable methods can improve the precision of psychiatric phenotypes, especially when the data structure is not straightforward. Using data from the British 1946 birth cohort, we compared summary scores with psychometric modeling based on the General Health Questionnaire (GHQ-28) scale for affective symptoms in an association analysis of 27 candidate genes (249 single-nucleotide polymorphisms (SNPs)). The psychometric method utilized a bi-factor model that partitioned the phenotype variances into five orthogonal latent variable factors, in accordance with the multidimensional data structure of the GHQ-28 involving somatic, social, anxiety and depression domains. Results showed that, compared with the summation approach, the affective symptoms defined by the bi-factor psychometric model had a higher number of associated SNPs of larger effect sizes. These results suggest that psychometrically defined mental health phenotypes can reflect the dimensions of complex phenotypes better than summation scores, and therefore offer a useful approach in genetic association investigations.

Original languageEnglish
Article numbere593
Pages (from-to)1-8
Number of pages8
JournalTranslational Psychiatry
Volume5
Early online date30 Jun 2015
DOIs
Publication statusPublished - 2015

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Psychometrics
Psychiatry
Molecular Biology
Phenotype
Affective Symptoms
Genetic Association Studies
Single Nucleotide Polymorphism
Genetic Research
Mood Disorders
Mental Health
Anxiety
Parturition
Depression
Health

Cite this

Xu, M. K., Gaysina, D., Barnett, J. H., Scoriels, L., van de Lagemaat, L. N., Wong, A., ... LHA Genetics Group (2015). Psychometric precision in phenotype definition is a useful step in molecular genetic investigation of psychiatric disorders. Translational Psychiatry, 5, 1-8. [e593]. https://doi.org/10.1038/tp.2015.86
Xu, M. K. ; Gaysina, D. ; Barnett, J. H. ; Scoriels, L. ; van de Lagemaat, L. N. ; Wong, A. ; Richards, M. ; Croudace, T. J. ; Jones, P. B. ; LHA Genetics Group. / Psychometric precision in phenotype definition is a useful step in molecular genetic investigation of psychiatric disorders. In: Translational Psychiatry. 2015 ; Vol. 5. pp. 1-8.
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abstract = "Affective disorders are highly heritable, but few genetic risk variants have been consistently replicated in molecular genetic association studies. The common method of defining psychiatric phenotypes in molecular genetic research is either a summation of symptom scores or binary threshold score representing the risk of diagnosis. Psychometric latent variable methods can improve the precision of psychiatric phenotypes, especially when the data structure is not straightforward. Using data from the British 1946 birth cohort, we compared summary scores with psychometric modeling based on the General Health Questionnaire (GHQ-28) scale for affective symptoms in an association analysis of 27 candidate genes (249 single-nucleotide polymorphisms (SNPs)). The psychometric method utilized a bi-factor model that partitioned the phenotype variances into five orthogonal latent variable factors, in accordance with the multidimensional data structure of the GHQ-28 involving somatic, social, anxiety and depression domains. Results showed that, compared with the summation approach, the affective symptoms defined by the bi-factor psychometric model had a higher number of associated SNPs of larger effect sizes. These results suggest that psychometrically defined mental health phenotypes can reflect the dimensions of complex phenotypes better than summation scores, and therefore offer a useful approach in genetic association investigations.",
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Xu, MK, Gaysina, D, Barnett, JH, Scoriels, L, van de Lagemaat, LN, Wong, A, Richards, M, Croudace, TJ, Jones, PB & LHA Genetics Group 2015, 'Psychometric precision in phenotype definition is a useful step in molecular genetic investigation of psychiatric disorders', Translational Psychiatry, vol. 5, e593, pp. 1-8. https://doi.org/10.1038/tp.2015.86

Psychometric precision in phenotype definition is a useful step in molecular genetic investigation of psychiatric disorders. / Xu, M. K. (Lead / Corresponding author); Gaysina, D.; Barnett, J. H.; Scoriels, L.; van de Lagemaat, L. N.; Wong, A.; Richards, M.; Croudace, T. J.; Jones, P. B.; LHA Genetics Group.

In: Translational Psychiatry, Vol. 5, e593, 2015, p. 1-8.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Psychometric precision in phenotype definition is a useful step in molecular genetic investigation of psychiatric disorders

AU - Xu, M. K.

AU - Gaysina, D.

AU - Barnett, J. H.

AU - Scoriels, L.

AU - van de Lagemaat, L. N.

AU - Wong, A.

AU - Richards, M.

AU - Croudace, T. J.

AU - Jones, P. B.

AU - LHA Genetics Group

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PY - 2015

Y1 - 2015

N2 - Affective disorders are highly heritable, but few genetic risk variants have been consistently replicated in molecular genetic association studies. The common method of defining psychiatric phenotypes in molecular genetic research is either a summation of symptom scores or binary threshold score representing the risk of diagnosis. Psychometric latent variable methods can improve the precision of psychiatric phenotypes, especially when the data structure is not straightforward. Using data from the British 1946 birth cohort, we compared summary scores with psychometric modeling based on the General Health Questionnaire (GHQ-28) scale for affective symptoms in an association analysis of 27 candidate genes (249 single-nucleotide polymorphisms (SNPs)). The psychometric method utilized a bi-factor model that partitioned the phenotype variances into five orthogonal latent variable factors, in accordance with the multidimensional data structure of the GHQ-28 involving somatic, social, anxiety and depression domains. Results showed that, compared with the summation approach, the affective symptoms defined by the bi-factor psychometric model had a higher number of associated SNPs of larger effect sizes. These results suggest that psychometrically defined mental health phenotypes can reflect the dimensions of complex phenotypes better than summation scores, and therefore offer a useful approach in genetic association investigations.

AB - Affective disorders are highly heritable, but few genetic risk variants have been consistently replicated in molecular genetic association studies. The common method of defining psychiatric phenotypes in molecular genetic research is either a summation of symptom scores or binary threshold score representing the risk of diagnosis. Psychometric latent variable methods can improve the precision of psychiatric phenotypes, especially when the data structure is not straightforward. Using data from the British 1946 birth cohort, we compared summary scores with psychometric modeling based on the General Health Questionnaire (GHQ-28) scale for affective symptoms in an association analysis of 27 candidate genes (249 single-nucleotide polymorphisms (SNPs)). The psychometric method utilized a bi-factor model that partitioned the phenotype variances into five orthogonal latent variable factors, in accordance with the multidimensional data structure of the GHQ-28 involving somatic, social, anxiety and depression domains. Results showed that, compared with the summation approach, the affective symptoms defined by the bi-factor psychometric model had a higher number of associated SNPs of larger effect sizes. These results suggest that psychometrically defined mental health phenotypes can reflect the dimensions of complex phenotypes better than summation scores, and therefore offer a useful approach in genetic association investigations.

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SN - 2158-3188

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