meta.subtract: Subtracts GWAS results from one or more cohorts from the meta-GWAS results

Description

Subtracts GWAS results from one or more cohorts from the meta-GWAS results. It can handle the following meta-analysis methods: fixed effects inverse variance weighted, fixed effects sample size weighted, and fixed effect sample size z-sore method. It also can account for single or double genomic control correction of the meta-GWAS results. It can be used for whole GWAS, but also for a limited set of SNPs or other genetic markers. A column with markername is required in both the meta-GWAS and cohort results files as well as the specific statistics used for the type of meta-analysis. Columns with effect and non-effect alleles are preferably given in the files. If this is not the case, it is assumed that statistics are given for the same alleles in the meta-GWAS and cohort results files.

Usage

meta.subtract(metafile, cohortfiles, metamethod = "FIV", lambda.meta = 1, 
      lambdas.cohort = 1, gc_meta = TRUE, calculate_lambda.meta = TRUE, 
      calculate_lambdas.cohort = TRUE, alternative = "alternative_headers.txt", 
	  save.as.data.frame = TRUE, 
	  savefile = "meta.results_corrected.with.MetaSubtract.txt.gz", 
      logfile = "MetaSubtract.log", dir=tempdir(), ...)

Arguments

metafile

Name of the file containing the results of the meta-GWAS

cohortfiles

Names of the files containing the results of the cohorts

metamethod

What kind of meta-GWAS method was used. Can be: FIV = fixed effect inverse variance meta-analysis (default); FSSW = fixed effect sample size weighted meta-analysis; FSZ = fixed effect sample size weighted z-score meta-analysis. In case of FIV, columns with effect size and its standard error are required in both the meta summary statistics file and the cohort results file. In case of FSZ, columns with z-score and sample size are required. In case of FSSW, columns with effect size and sample size are required.

lambda.meta

Genomic control lambda of the meta-results. If no or single genomic control correction was applied, lambda.meta should be set to 1. Default = 1.

lambdas.cohort

Genomic control lambdas of the cohorts. If no genomic control correction was applied, lambdas.cohort should be set to 1 for each cohort. Default = 1.

gc_meta

Apply genomic control to the corrected meta-results? If FALSE, corrected meta will not be corrected for genomic control. Default = TRUE.

calculate_lambda.meta

Calculate genomic control lambda from the corrected meta-results? If FALSE and gc_meta=TRUE, lambda.meta will be used for genomic control correction of the corrected meta-results. If gc_meta=FALSE, this will be ignored. Default = TRUE.

calculate_lambdas.cohort

Calculate genomic control lambdas from the cohort results? If TRUE, lambdas.cohort will be ignored. Default = TRUE.

alternative

Name of the file containing alternative codes for the column headers in the GWAS files.

save.as.data.frame

Set to FALSE if you don't want to save the corrected meta-GWAS summary statistics as a data frame. In that case the results will only be saved to file. Default = TRUE.

savefile

Name of file to which corrected meta-GWAS results are saved. If NULL or NA, results are not saved to file. Default filename = "meta.results_corrected.with.MetaSubtract.txt".

logfile

Name of the file where information on the run is saved.

dir

Directory where the meta and cohort GWAS files are stored.

...

Other possible arguments for reading in the files, e.g. sep, naStrings, or nrow

Value

A data frame with the corrected meta-GWAS results. This data frame contains the same genetic markers as in the input meta-GWAS file. If a marker was not present in the cohort(s) files, the result of that marker will be the same as in the input meta-GWAS file. If a marker was present in the cohort(s) but not in the meta-GWAS file, it will not be included in the output.

References

Nolte et al. (2017). Missing heritability: is the gap closing? An analysis of 32 complex traits in the Lifelines Cohort Study. Eur J Hum Genet. 2017;25:877-885.

Examples

Run this code

# NOT RUN {
metafile="meta_results.txt"
cohortfiles=c("cohort1_results.txt","cohort2_results.txt","cohort3_results.txt")
# Subtract summary statistics of the two cohorts from the meta summary statistics in metafile.
# Genomic control correction of the imported meta summary statistics will be undone using 
# lambda.meta. Genomic control will be applied to the corrected meta summary statistics 
# (gc_meta=TRUE by default) based on a lambda derived from the new meta summary statistics 
# (calculate_lambda.meta=TRUE by default). The cohorts summary statistics will always be first 
# corrected for genomic control before subtracting them from the meta summary statistics. By 
# default, the lambda will be calculated from the data (calculate_lambdas.cohort=TRUE by default).
m1<-meta.subtract(metafile=metafile, cohortfiles=cohortfiles, lambda.meta=1.046, dir=tempdir())
head(m1)

# If you would like pre-set genomic control lambdas (e.g. in case you use only part of the data), 
# set calculate_lambda.meta=FALSE and calculate_lambdas.cohort=FALSE and define the 
# lambda for the meta summary statistics by lambda.meta, and for the cohort(s) by lambda.cohorts.
m2<-meta.subtract(metafile=metafile, cohortfiles=cohortfiles, lambda.meta=1.046, 
      lambdas.cohort=c(1.0012,1.03), calculate_lambda.meta=FALSE, calculate_lambdas.cohort=FALSE, 
	  dir=tempdir())
head(m2)

# If you do not want to apply genomic control, set gc_meta=FALSE,  
# calculate_lambda.meta=FALSE and calculate_lambdas.cohort=FALSE.
# lambda.meta=1 and lambda.cohorts=c(1,1) are default values.
m3<-meta.subtract(metafile=metafile, cohortfiles=cohortfiles, gc_meta=FALSE, 
      calculate_lambda.meta=FALSE, calculate_lambdas.cohort=FALSE, 
	  dir=tempdir())
head(m3)

cohortfiles=c("cohort1_results.txt","cohort2_results.txt")
# If you performed a sqrt(smaple sizes) weighted meta-analysis on the z-scores, change the
# meta-analysis method by 'metamethod=FSZ'.
m4<-meta.subtract(metafile=metafile, cohortfiles=cohortfiles, metamethod="FSZ", 
      lambda.meta=1.046, dir=tempdir())
head(m4)

# }