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Last updated: 2021-03-04

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Knit directory: PSYMETAB/

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Repository version: c96d28f

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Unstaged changes:
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Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

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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	c96d28f	Jenny Sjaarda	2021-03-04	Rebuilding index
Rmd	941b66d	Jenny Sjaarda	2021-03-02	add new Rmd files and respective html files
Rmd	85955b3	Jenny Sjaarda	2020-06-23	Update site
html	85955b3	Jenny Sjaarda	2020-06-23	Update site
html	46477dd	Jenny Sjaarda	2019-12-06	Build site.
Rmd	b503ef0	Sjaarda Jennifer Lynn	2019-12-06	add more details to website
html	d1e539c	Jenny Sjaarda	2019-12-06	Build site.
Rmd	487b5f5	Sjaarda Jennifer Lynn	2019-12-06	update website, add qc description
html	9f1ba5e	Jenny Sjaarda	2019-12-06	Build site.
Rmd	d480e35	Jenny	2019-12-04	misc annotations
html	12223b3	Jenny Sjaarda	2019-12-02	Build site.
Rmd	dafe346	Jenny	2019-12-02	modify index
Rmd	0dd02a7	Jenny	2019-12-02	modify website
html	2849dcb	Jenny Sjaarda	2019-12-02	wflow_git_commit(all = T)
Rmd	3d512c2	Sjaarda Jennifer Lynn	2019-12-02	modify website and drake plans
Rmd	8e743a8	Sjaarda Jennifer Lynn	2019-11-27	drakeplan
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.

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This website contains all information and results about this research project.

Project details and overview can be found in the About page.

See Setup for details on setting up the project and the various analyses for this project.

See Drake plans for a visual representation of analytical pipelines.

Useful shortcuts

1. Data sources for details on data sources and origins.
2. Overview of processing in genomestudio.
3. Genetic quality control.
4. Phenotypic quality control.
5. Genetic data extraction protocol.
6. GWAS methods.
7. GWAS results.

Powered by drake + workflowr

Useful papers

• Clozapine-induced weight gain: a study in monozygotic twins and same-sex sib pairs.
• A new genetic locus for antipsychotic-induced weight gain: A genome-wide study of first-episode psychosis patients using amisulpride (from the OPTiMiSE cohort).
• Psychotropic drug-induced weight gain and other metabolic complications in a Swiss psychiatric population.
• Pharmacogenetics of antipsychotic-induced weight gain: review and clinical implications.

Current Analyses

GWAS in PSYMETAB (our study)

• Run within various subsets.
• Subsets were defined based on drug use, for various drug groups of interest (we had 9 groups of drugs that we analyzed).
• Phenotypes included various measures of the evolution of BMI: BMI change, BMI slope, weighted slope for SE, etc.
• Restricted to only European subset.
• Phenotypes were residualized for: age, age^2, sex, PC1-20, follow-up time, follow-up time^2, and baseline BMI (Zoltan: we discussed this last time, but to again clarify - should follow-up time be included, it seems redundant if we are analyzing slope which already considers time between BMI measures).

GWAS in UKBB

• Phenotype: BMI slope.
• GWAS restricted to participants having at least 2 BMI measures restricted to unrelated, British individuals.
• Phenotype was first residualized for: age, sex, PC1-40, and baseline BMI

COMPARISON BETWEEN PSYMETAB and UKBB:

• In PSYMETAB, I took only endpoints involving slope since this would represent the best comparison to UKBB.
• I extracted nominally significant hits in PSYMETAB (p < 5e-06) (as there were very few less with p< 5e-08).
• For these SNPs, I compared with UKBB using a standard heterogeneity statistic.
• Limited to only GW-sig results based on het p-value.
• Identified 68 GW-significant SNPs (i.e. SNPs that show significant difference between some PSYMETAB subset and UKBB based on a BMI slope measure, for only those SNPs in PSYMETAB that have p < 5e-06).

1. Test top SNPs from our GWAS (within drug subgroups) with BMI in UKBB within a group defined as under the same drug as the association in our psychiatric cohort (using drug information data in UKBB). Note that in contrast to our psychiatric cohort, we don’t have information for how long a UKBB participant has been taking a specific drug.
2. To justify (1), ensure that BMI is a good proxy for change in BMI (i.e. the phenotype used in the psychiatric cohort), correlate BMI at end of follow-up with BMI slope in our psychiatric cohort.
3. For rare (0.01 < MAF < 0.05) SNPs, perform permutation test (i.e. shuffle phenotype ~1M times and perform same association: pheno ~ SNP + covars) to see if the effect found with the real data (i.e. unshuffled phenotype) is significantly larger than using the 1M shuffled phenotypes and not just some artifact of low AF.
4. Meta-analyze GWAS results across drug subgroups and use Cochran’s Q test (i.e. test for heterogeneity) to determine effects differ across subgroups.

Acknowledgements

Primary analysis performed by: Jenny Sjaarda. Project supervisor: Professor Chin Bin Eap. Analysis support: Professor Zoltan Kutalik.

This work has been supported by the Swiss National Science Foundation.

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