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Last updated: 2021-06-01

Checks: 7 0

Knit directory: PSYMETAB/

This reproducible R Markdown analysis was created with workflowr (version 1.6.0). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20191126)

The command set.seed(20191126) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: f231b56

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:

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---

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	ea3770f	jennysjaarda	2021-03-05	Update “about” page to include SNF description
html	e8b9660	Jenny Sjaarda	2021-03-04	Build site.
Rmd	941b66d	Jenny Sjaarda	2021-03-02	add new Rmd files and respective html files
html	9f1ba5e	Jenny Sjaarda	2019-12-06	Build site.
html	97b2b48	Jenny	2019-11-26	Build site.
Rmd	2db9bcf	Jenny	2019-11-26	wflow_publish(all = T)
html	2db9bcf	Jenny	2019-11-26	wflow_publish(all = T)
html	5fb939e	Jenny Sjaarda	2019-11-26	Build site.
Rmd	7bae873	Jenny Sjaarda	2019-11-26	Start workflowr project.

---

Last updated: 2021-06-01

Code version: f231b562f65babff1f6e34bdff16ffe422779d1e

Introduction and Rationale

• Antipsychotic induced weight gain is a common side effect of antipsychotic treatement.
• However, this observation varies considerably across patients, and has been shown to be linked to many factors, including type of treatment, pre-treatment body mass index (BMI), sex and age.
• Beyond these variables, significant variability across individuals remains, pointing to a potential role of inter-individual genetic variability in antipsychotic induce weight gain.
• Indeed, the heritability (h2) for antipsychotic induced weight gain has been estimated to be 0.8 (Theisen et al., 2005).
• Despite these observations, few genome-wide significant loci have been discovered. Identification of genetic polymorphisms responsible for antipsychotic induced weight gain is a valuable research endeavor which could lead to both improved treatment and more personalized interventions to combat this harmful and debilitating side-effect of antipsychotics and improve quality of life.

Methods

• In this study, we sought to identify novel genetic polymorphisms for antipsychotic induced weight gain across a range of antipsychotic treatments.
• These treatments included olanzapine, clozapine, valproate, quetiapine, risperidone, amisulpride, among others.
• The study population included 2767 psychiatric participants who have been followed up to XX years with up to XX number of visits, and under at least one antipsychotic treatment at each visit.
• Standard clinical measures were assessed and recorded at each visit, including weight-gain, to evaluate the evolution of BMI overtime.
• All participants were genotyped on the Global Screening Array (GSA) v2 with multiple disease option (MD) chip.
• After standard quality control filters (i.e. missingness, sex check, excess relatedness, minor allele frequency and Hardy-Weinberg) and imputation were performed, our study sample consisted of 2647 samples and 10382351 SNPs.
• We chose to limit the analyses to only the European subset comprising 2116 samples.
• European ancestry was determined using snpweights, a software for inferring genome-wide genetic ancestry using SNP weights precomputed from large external reference panels (Chen et al., 2013).
• We analyzed drugs both separately and combined (based on mode of action and biological pathways).
• Additionally, we investigated the evolution of BMI using various definitions, including delta change in BMI over time, slope of BMI over time (estimated using a linear model with BMI regressed on time between follow-up visits, in days), and weighted slope of BMI over time (weighted by the inverse SE).
• These definitions were then repeated restricting to only 6, 3 and 1 month of follow-up, as weight gain has been shown to be greatest in the first 6 months and plateau with continual antipsychotic use.
• We also utilized the UKBiobnak (UKB), a very large population-based cohort, as a control population. Briefly, we included 31056 participants with at least two measures for BMI and performed a GWAS with the change in BMI over time (as in the psychiatric cohort) as the dependent phenotype.
• Analyses were restricted to British individuals. These results served as a control GWAS to ensure that GW-significant findings within the psychiatric cohort, truly represent antipsychotic-induced weight gain-associated loci as apposed weight gain-associated loci, in general.

Results

• We analyzed drugs both separately and combined (based on mode of action and biological pathways).
• Additionally, we investigated the evolution of BMI using various definitions, including delta change in BMI over time, slope of BMI over time (estimated using a linear model with BMI regressed on time between follow-up visits, in days), and weighted slope of BMI over time (weighted by the inverse SE).
• These definitions were then repeated restricting to only 6, 3 and 1 month of follow-up, as weight gain has been shown to be greatest in the first 6 months and plateau with continual antipsychotic use.
• We also utilized the UKBiobnak (UKB), a very large population-based cohort, as a control population. Briefly, we included 31056 participants with at least two measures for BMI and performed a GWAS with the change in BMI over time (as in the psychiatric cohort) as the dependent phenotype.
• Analyses were restricted to British individuals. These results served as a control GWAS to ensure that GW-significant findings within the psychiatric cohort, truly represent antipsychotic-induced weight gain-associated loci as apposed weight gain-associated loci, in general.

Discussion

• In summary, we identified 15 loci showing a GW-significant difference between the UKBB and our psychiatric cohort for four different antipsychotic drugs (olanzapine, clozapine, risperidone, and amisulpride) whereby a stronger (p < 5e-06) association was identified between these loci and BMI slope in the psychiatric cohort compared to the general population in the UKBB.
• These results suggest that these loci may represent a weight-inducing effect specific to antipsychotic use rather than some wide-ranging mechanism present in the general population.
• Future analyses will seek to replicate these findings in a larger cohort, compare these findings to a GWAS of BMI in both the general population (using the GIANT consortium, for example) and within our psychiatric cohort, and finally expand the phenotypes of interest to biomarker levels including LDL, HDL, creatine, and glucose levels.

Session information

sessionInfo()

R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS: /data/sgg2/jenny/bin/R-3.5.3/lib64/R/lib/libRblas.so
LAPACK: /data/sgg2/jenny/bin/R-3.5.3/lib64/R/lib/libRlapack.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] workflowr_1.6.0

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.3      rprojroot_1.3-2 packrat_0.5.0   digest_0.6.25  
 [5] later_1.0.0     R6_2.4.1        backports_1.1.6 git2r_0.26.1   
 [9] magrittr_1.5    evaluate_0.14   stringi_1.4.5   rlang_0.4.5    
[13] fs_1.3.1        promises_1.1.0  whisker_0.4     rmarkdown_1.18 
[17] tools_3.5.3     stringr_1.4.0   glue_1.4.0      yaml_2.2.0     
[21] httpuv_1.5.2    xfun_0.11       compiler_3.5.3  htmltools_0.4.0
[25] knitr_1.26