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

Xu MK, Gaysina D, Barnett JH, Scoriels L, van de Lagemaat LN, Wong A, Richards M, Croudace TJ, Jones PB, LHA Genetics Gro - Transl Psychiatry (2015)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.

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.

No MeSH data available.


Related in: MedlinePlus

Psychometric model of General Health Questionnaire 28-item version (GHQ-28) items with a single-nucleotide polymorphism (SNP) predictor. Oval shapes are latent variables representing global and specific affective disorder domains. Rectangular shapes are observed variables including both the SNP predictor and GHQ-28 items that are the basis of the latent variables. Arrows leading from a SNP variable to latent variables represent the regression path from the SNP predictor to global as well as specific phenotype dimensions. The arrows between the latent variable to the observed variables indicate the strength of the relationship between the two, represented by standardized factor loadings. The standardized factor loadings are based on a phenotype-only model (excluding the SNP predictor variable from the model). The model fit indices were as follows: 1643.081 (273 degree of freedom) for X2, 0.061 for Root Mean Square Error of Approximation (RMSEA), 0.968 for Comparative Fit Index (CFI) and 0.961 for Tucker–Lewis Index (TLI). *The indicator ‘headaches' was based on the sum of two highly correlated items. This is to avoid convergence problems caused by the high colinearity between the two items. **Similarly, ‘sleep problems' were also based on two substantially correlated items. Both items were still treated as ordinal measures in model estimation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4490295&req=5

fig1: Psychometric model of General Health Questionnaire 28-item version (GHQ-28) items with a single-nucleotide polymorphism (SNP) predictor. Oval shapes are latent variables representing global and specific affective disorder domains. Rectangular shapes are observed variables including both the SNP predictor and GHQ-28 items that are the basis of the latent variables. Arrows leading from a SNP variable to latent variables represent the regression path from the SNP predictor to global as well as specific phenotype dimensions. The arrows between the latent variable to the observed variables indicate the strength of the relationship between the two, represented by standardized factor loadings. The standardized factor loadings are based on a phenotype-only model (excluding the SNP predictor variable from the model). The model fit indices were as follows: 1643.081 (273 degree of freedom) for X2, 0.061 for Root Mean Square Error of Approximation (RMSEA), 0.968 for Comparative Fit Index (CFI) and 0.961 for Tucker–Lewis Index (TLI). *The indicator ‘headaches' was based on the sum of two highly correlated items. This is to avoid convergence problems caused by the high colinearity between the two items. **Similarly, ‘sleep problems' were also based on two substantially correlated items. Both items were still treated as ordinal measures in model estimation.

Mentions: The psychometric phenotype of affective symptoms was specified in the form of a bi-factor model (Figure 1). The phenotype outcome variables were posited as latent variables. The bi-factor specification allows the questionnaire items to measure directly both the global factor and four specific factors representing each subdimension of affective symptoms. The factors in the bi-factor model are orthogonal, with covariances among factors specified to be zero. That is, under the bi-factor specification, the global factor is based on information corresponding to the global affective symptom phenotype and is no longer ‘contaminated' by variances that are specific to the subdimensions. Similarly, subdimensions that otherwise embedded in a single global measure according to the sum score approach are now modeled separately as distinct, nonoverlapping dimension-specific latent factors.


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

Xu MK, Gaysina D, Barnett JH, Scoriels L, van de Lagemaat LN, Wong A, Richards M, Croudace TJ, Jones PB, LHA Genetics Gro - Transl Psychiatry (2015)

Psychometric model of General Health Questionnaire 28-item version (GHQ-28) items with a single-nucleotide polymorphism (SNP) predictor. Oval shapes are latent variables representing global and specific affective disorder domains. Rectangular shapes are observed variables including both the SNP predictor and GHQ-28 items that are the basis of the latent variables. Arrows leading from a SNP variable to latent variables represent the regression path from the SNP predictor to global as well as specific phenotype dimensions. The arrows between the latent variable to the observed variables indicate the strength of the relationship between the two, represented by standardized factor loadings. The standardized factor loadings are based on a phenotype-only model (excluding the SNP predictor variable from the model). The model fit indices were as follows: 1643.081 (273 degree of freedom) for X2, 0.061 for Root Mean Square Error of Approximation (RMSEA), 0.968 for Comparative Fit Index (CFI) and 0.961 for Tucker–Lewis Index (TLI). *The indicator ‘headaches' was based on the sum of two highly correlated items. This is to avoid convergence problems caused by the high colinearity between the two items. **Similarly, ‘sleep problems' were also based on two substantially correlated items. Both items were still treated as ordinal measures in model estimation.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4490295&req=5

fig1: Psychometric model of General Health Questionnaire 28-item version (GHQ-28) items with a single-nucleotide polymorphism (SNP) predictor. Oval shapes are latent variables representing global and specific affective disorder domains. Rectangular shapes are observed variables including both the SNP predictor and GHQ-28 items that are the basis of the latent variables. Arrows leading from a SNP variable to latent variables represent the regression path from the SNP predictor to global as well as specific phenotype dimensions. The arrows between the latent variable to the observed variables indicate the strength of the relationship between the two, represented by standardized factor loadings. The standardized factor loadings are based on a phenotype-only model (excluding the SNP predictor variable from the model). The model fit indices were as follows: 1643.081 (273 degree of freedom) for X2, 0.061 for Root Mean Square Error of Approximation (RMSEA), 0.968 for Comparative Fit Index (CFI) and 0.961 for Tucker–Lewis Index (TLI). *The indicator ‘headaches' was based on the sum of two highly correlated items. This is to avoid convergence problems caused by the high colinearity between the two items. **Similarly, ‘sleep problems' were also based on two substantially correlated items. Both items were still treated as ordinal measures in model estimation.
Mentions: The psychometric phenotype of affective symptoms was specified in the form of a bi-factor model (Figure 1). The phenotype outcome variables were posited as latent variables. The bi-factor specification allows the questionnaire items to measure directly both the global factor and four specific factors representing each subdimension of affective symptoms. The factors in the bi-factor model are orthogonal, with covariances among factors specified to be zero. That is, under the bi-factor specification, the global factor is based on information corresponding to the global affective symptom phenotype and is no longer ‘contaminated' by variances that are specific to the subdimensions. Similarly, subdimensions that otherwise embedded in a single global measure according to the sum score approach are now modeled separately as distinct, nonoverlapping dimension-specific latent factors.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Cambridge, UK.

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.

No MeSH data available.


Related in: MedlinePlus