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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

Density plots of single-nucleotide polymorphism (SNP) effect sizes. The x axis represents effect size in terms of the percentage of phenotypic variance explained by a single SNP. The y axis represents density of effect size.
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fig2: Density plots of single-nucleotide polymorphism (SNP) effect sizes. The x axis represents effect size in terms of the percentage of phenotypic variance explained by a single SNP. The y axis represents density of effect size.

Mentions: Distributions of the effect size frequencies were estimated as densities (Figure 2) and plotted separately for the sum score approach and the bi-factor approach. The area under each curve is 1 and represents the probability of observing an effect size range. As shown in Figure 2, across all phenotype domains, for effect sizes of smaller magnitude, the density of the smallest set of effect sizes was higher for the sum score approach than for the bi-factor approach. In particular, no SNP predictor explained more than 1% of the phenotype variance on the basis of the sum score approach. On the other hand, the density of larger effect sizes was higher for the bi-factor approach. Thus, the bi-factor approach in general yielded a larger number of, and in general, larger effect sizes. To illustrate this finding for a particular candidate gene, the effects of the 12 SNPs of the DLG4 gene on global affective symptoms and GHQ-28 subscales are shown in Figure 3. Compared with the sum score phenotype, affective symptoms defined by the bi-factor approach had in general larger effect sizes. This was especially the case for the anxiety-symptom subscale.


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)

Density plots of single-nucleotide polymorphism (SNP) effect sizes. The x axis represents effect size in terms of the percentage of phenotypic variance explained by a single SNP. The y axis represents density of effect size.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Density plots of single-nucleotide polymorphism (SNP) effect sizes. The x axis represents effect size in terms of the percentage of phenotypic variance explained by a single SNP. The y axis represents density of effect size.
Mentions: Distributions of the effect size frequencies were estimated as densities (Figure 2) and plotted separately for the sum score approach and the bi-factor approach. The area under each curve is 1 and represents the probability of observing an effect size range. As shown in Figure 2, across all phenotype domains, for effect sizes of smaller magnitude, the density of the smallest set of effect sizes was higher for the sum score approach than for the bi-factor approach. In particular, no SNP predictor explained more than 1% of the phenotype variance on the basis of the sum score approach. On the other hand, the density of larger effect sizes was higher for the bi-factor approach. Thus, the bi-factor approach in general yielded a larger number of, and in general, larger effect sizes. To illustrate this finding for a particular candidate gene, the effects of the 12 SNPs of the DLG4 gene on global affective symptoms and GHQ-28 subscales are shown in Figure 3. Compared with the sum score phenotype, affective symptoms defined by the bi-factor approach had in general larger effect sizes. This was especially the case for the anxiety-symptom subscale.

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