Dataset Information

GW-SEM: A Statistical Package to Conduct Genome-Wide Structural Equation Modeling.

ABSTRACT: Improving the accuracy of phenotyping through the use of advanced psychometric tools will increase the power to find significant associations with genetic variants and expand the range of possible hypotheses that can be tested on a genome-wide scale. Multivariate methods, such as structural equation modeling (SEM), are valuable in the phenotypic analysis of psychiatric and substance use phenotypes, but these methods have not been integrated into standard genome-wide association analyses because fitting a SEM at each single nucleotide polymorphism (SNP) along the genome was hitherto considered to be too computationally demanding. By developing a method that can efficiently fit SEMs, it is possible to expand the set of models that can be tested. This is particularly necessary in psychiatric and behavioral genetics, where the statistical methods are often handicapped by phenotypes with large components of stochastic variance. Due to the enormous amount of data that genome-wide scans produce, the statistical methods used to analyze the data are relatively elementary and do not directly correspond with the rich theoretical development, and lack the potential to test more complex hypotheses about the measurement of, and interaction between, comorbid traits. In this paper, we present a method to test the association of a SNP with multiple phenotypes or a latent construct on a genome-wide basis using a diagonally weighted least squares (DWLS) estimator for four common SEMs: a one-factor model, a one-factor residuals model, a two-factor model, and a latent growth model. We demonstrate that the DWLS parameters and p-values strongly correspond with the more traditional full information maximum likelihood parameters and p-values. We also present the timing of simulations and power analyses and a comparison with and existing multivariate GWAS software package.

SUBMITTER: Verhulst B

PROVIDER: S-EPMC5423544 | biostudies-literature | 2017 May

REPOSITORIES: biostudies-literature

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GW-SEM: A Statistical Package to Conduct Genome-Wide Structural Equation Modeling.

Verhulst Brad B Maes Hermine H HH Neale Michael C MC

Behavior genetics 20170315 3

Improving the accuracy of phenotyping through the use of advanced psychometric tools will increase the power to find significant associations with genetic variants and expand the range of possible hypotheses that can be tested on a genome-wide scale. Multivariate methods, such as structural equation modeling (SEM), are valuable in the phenotypic analysis of psychiatric and substance use phenotypes, but these methods have not been integrated into standard genome-wide association analyses because ...[more]

PMID: 28299468

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Project description:Vaccination is considered one of the solutions to the Coronavirus Disease 2019 (COVID-19) pandemic. However, a small proportion of the population were fully vaccinated in Benin (20.9%) and Senegal (7.6%) by December 2022. This study explores the determinants of intent to vaccinate. This was a cross-sectional, descriptive, and analytical study of 865 Beninese and 813 Senegalese aged 18 years and older. Marginal quota sampling by age, gender and region was adopted. Data collection, using a survey instrument based on the Random Digit Dialing method, was conducted from December 24, 2020, to January 16, 2021, in Senegal and from March 29 to May 14, 2021, in Benin. The questionnaire used the Theory of Planned Behavior and the Health Belief Model. The influence of factors was assessed using a structural equation model based on a diagonally weighted least squares estimator to account for ordered categorical data (Likert scales). In Benin and Senegal, the intention to vaccinate against COVID-19 is influenced by distinct factors. In Benin, social influence (β = 0.42, p = 0.003) and perception of vaccine safety (β = -0.53, p<0.001) play pivotal roles, suggesting those socially influenced have a higher vaccination intention. In Senegal, vaccination intentions are primarily driven by positive attitudes towards the vaccine (β = 0.65, p = 0.013) and social influence (β = 0.25, p = 0.048). This underscores the importance of individual beliefs, personal perceptions, and supportive social contexts in decision-making. Notably, positive vaccination attitudes and perceptions in both countries are strongly tied to increased social influence. While nuances exist between Benin and Senegal regarding factors influencing COVID-19 vaccination intentions, both nations underscore the pivotal roles of social influence and individual vaccination perceptions. Emphasizing trust in vaccine safety and promoting positive attitudes through effective communication are crucial for enhancing vaccination uptake in these West African countries.

Dataset Information

GW-SEM: A Statistical Package to Conduct Genome-Wide Structural Equation Modeling.

Publications

GW-SEM: A Statistical Package to Conduct Genome-Wide Structural Equation Modeling.

Similar Datasets

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