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Powerful and Efficient Strategies for Genetic Association Testing of Symptom and Questionnaire Data in Psychiatric Genetic Studies.

ABSTRACT: Genetic studies of psychiatric disorders often deal with phenotypes that are not directly measurable. Instead, researchers rely on multivariate symptom data from questionnaires and surveys like the PTSD Symptom Scale (PSS) and Beck Depression Inventory (BDI) to indirectly assess a latent phenotype of interest. Researchers subsequently collapse such multivariate questionnaire data into a univariate outcome to represent a surrogate for the latent phenotype. However, when a causal variant is only associated with a subset of collapsed symptoms, the effect will be challenging to detect using the univariate outcome. We describe a more powerful strategy for genetic association testing in this situation that jointly analyzes the original multivariate symptom data collectively using a statistical framework that compares similarity in multivariate symptom-scale data from questionnaires to similarity in common genetic variants across a gene. We use simulated data to demonstrate this strategy provides substantially increased power over standard approaches that collapse questionnaire data into a single surrogate outcome. We also illustrate our approach using GWAS data from the Grady Trauma Project and identify genes associated with BDI not identified using standard univariate techniques. The approach is computationally efficient, scales to genome-wide studies, and is applicable to correlated symptom data of arbitrary dimension.

SUBMITTER: Holleman AM

PROVIDER: S-EPMC6525248 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature

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Powerful and Efficient Strategies for Genetic Association Testing of Symptom and Questionnaire Data in Psychiatric Genetic Studies.

Holleman Aaron M AM Broadaway K Alaine KA Duncan Richard R Todor Andrei A Almli Lynn M LM Bradley Bekh B Ressler Kerry J KJ Ghosh Debashis D Mulle Jennifer G JG Epstein Michael P MP

Scientific reports 20190517 1

Genetic studies of psychiatric disorders often deal with phenotypes that are not directly measurable. Instead, researchers rely on multivariate symptom data from questionnaires and surveys like the PTSD Symptom Scale (PSS) and Beck Depression Inventory (BDI) to indirectly assess a latent phenotype of interest. Researchers subsequently collapse such multivariate questionnaire data into a univariate outcome to represent a surrogate for the latent phenotype. However, when a causal variant is only a ...[more]

PMID: 31101869

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Project description:BackgroundEfficient screening questionnaires are useful in general practice. Computerized adaptive testing (CAT) is a method to improve the efficiency of questionnaires, as only the items that are particularly informative for a certain responder are dynamically selected.ObjectiveThe objective of this study was to test whether CAT could improve the efficiency of the Four-Dimensional Symptom Questionnaire (4DSQ), a frequently used self-report questionnaire designed to assess common psychosocial problems in general practice.MethodsA simulation study was conducted using a sample of Dutch patients visiting a general practitioner (GP) with psychological problems (n=379). Responders completed a paper-and-pencil version of the 50-item 4DSQ and a psychometric evaluation was performed to check if the data agreed with item response theory (IRT) assumptions. Next, a CAT simulation was performed for each of the four 4DSQ scales (distress, depression, anxiety, and somatization), based on the given responses as if they had been collected through CAT. The following two stopping rules were applied for the administration of items: (1) stop if measurement precision is below a predefined level, or (2) stop if more than half of the items of the subscale are administered.ResultsIn general, the items of each of the four scales agreed with IRT assumptions. Application of the first stopping rule reduced the length of the questionnaire by 38% (from 50 to 31 items on average). When the second stopping rule was also applied, the total number of items could be reduced by 56% (from 50 to 22 items on average).ConclusionsCAT seems useful for improving the efficiency of the 4DSQ by 56% without losing a considerable amount of measurement precision. The CAT version of the 4DSQ may be useful as part of an online assessment to investigate the severity of mental health problems of patients visiting a GP. This simulation study is the first step needed for the development a CAT version of the 4DSQ. A CAT version of the 4DSQ could be of high value for Dutch GPs since increasing numbers of patients with mental health problems are visiting the general practice. In further research, the results of a real-time CAT should be compared with the results of the administration of the full scale.

Project description:Objective: Mental illness often interferes with daily functioning and an individual's pattern of psychiatric signs and symptoms may predict risk of future disability. Understanding the linkage between psychiatric symptoms and impaired functioning is critical for accurate rehabilitation planning and legal assessment. Here, we investigated the stability of functional impairment measures over 18 months and their association with psychiatric symptoms. Moreover, we developed a clinical self-report measure that allows estimation of functional impairment levels over 18 month observation periods. Methods: Consecutively treated outpatients and daycare patients (N = 155) from several psychiatric units in Switzerland completed the Dissociative Experiences Scale, Somatoform Dissociation Questionnaire, Multidimensional Inventory for Dissociation, Beck Depression Inventory, Brief Symptom Inventory, and WHO Disability Assessment Schedule at baseline, 6, 12, and 18 month follow-up examinations. The association between symptoms functional impairment over time was investigated using longitudinal linear mixed models. Penalized regression was used to identify questionnaire items that best predicted functional impairment. Results: We found high stability in the extent of functional impairment over 18 months. Fear of negative evaluation, fatigue, concentration problems, negative alterations in mood, and dissociative symptoms showed the strongest association with functional impairment measures. The empirically derived scale for functional impairment prediction explained between 0.62 and 0.77 of the variance in disability across various life domains. Conclusion: Given the capability for somatic and mental symptoms associated with social anxiety, depression, and dissociation to predict future disability, these symptoms have strong potential for guiding rehabilitation planning and prognostic evaluation in insurance medicine. The Functional Impairment Prediction Scale may serve as a valuable, empirical-based extension in legal assessments of how work capacity is affected by psychological factors.

Project description:MotivationMany GWAS conducted in the past decade have identified tens of thousands of disease related variants, which in total explained only part of the heritability for most traits. There remain many more genetics variants with small effect sizes to be discovered. This has motivated the development of sequencing studies with larger sample sizes and increased resolution of genotyped variants, e.g., the ongoing NHLBI Trans-Omics for Precision Medicine (TOPMed) whole genome sequencing project. An alternative approach is the development of novel and more powerful statistical methods. The current dominating approach in the field of GWAS analysis is the "single trait single variant" association test, despite the fact that most GWAS are conducted in deeply-phenotyped cohorts with many correlated traits measured. In this paper, we aim to develop rigorous methods that integrate multiple correlated traits and multiple variants to improve the power to detect novel variants. In recognition of the difficulty of accessing raw genotype and phenotype data due to privacy and logistic concerns, we develop methods that are applicable to publicly available GWAS summary data.ResultsWe build rigorous statistical models for GWAS summary statistics to motivate novel multi-trait SNP-set association tests, including variance component test, burden test and their adaptive test, and develop efficient numerical algorithms to quickly compute their analytical P-values. We implement the proposed methods in an open source R package. We conduct thorough simulation studies to verify the proposed methods rigorously control type I errors at the genome-wide significance level, and further demonstrate their utility via comprehensive analysis of GWAS summary data for multiple lipids traits and glycemic traits. We identified many novel loci that were not detected by the individual trait based GWAS analysis.Availability and implementationWe have implemented the proposed methods in an R package freely available at http://www.github.com/baolinwu/MSKAT.Supplementary informationSupplementary data are available at Bioinformatics online.

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Powerful and Efficient Strategies for Genetic Association Testing of Symptom and Questionnaire Data in Psychiatric Genetic Studies.

Publications

Powerful and Efficient Strategies for Genetic Association Testing of Symptom and Questionnaire Data in Psychiatric Genetic Studies.

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