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

M K Xu,A Wong,L N Van De Lagemaat,The Lha Genetics Group ,D Gaysina,T J Croudace,P B Jones,M Richards,J H Barnett,L Scoriels

doi:10.1038/tp.2015.86

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.

Highlights

IntroductionClassification of affective disorder is usually based on selfreported symptoms intended to capture psychopathological syndromes through questionnaire responses, clinical ratings and evaluations or by field-trained interviewers
We systematically evaluated the effects of SNPs of candidate genes on affective symptom outcomes defined through traditional sum score and psychometric approaches
The larger effect sizes observed in the bi-factor phenotype analysis were consistent with the application of a more appropriate method for phenotype definition that takes into account the complex structure of the GHQ-28 subscales

Summary

Introduction

Classification of affective disorder is usually based on selfreported symptoms intended to capture psychopathological syndromes through questionnaire responses, clinical ratings and evaluations or by field-trained interviewers. Psychiatric phenotypes defined this way may be especially susceptible to variance introduced by testing methods or other potentially bias-inducing elements that are not primarily related to the disorder itself. Some work has previously been done to develop statistical tools for the detection of genetic associations in both candidate gene and GWAS.[10,12,13,14,15] For example, van der Sluis et al.[14] have developed the TATES method that combines P-values based on correlations between components of multivariate phenotype. The TATES method has demonstrated higher statistical power compared with methods based on composite scores or multivariate analysis of variance

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Conclusion