Evaluate imputation quality

Evaluate

Usage

run_imputability(
  df,
  gds,
  window,
  flankWidth,
  ids,
  method = c("decorrelate", "Ledoit-Wolf", "OAS", "Touloumis", "Schafer-Strimmer"),
  lambda = NULL,
  quiet = FALSE,
  ...
)

Arguments

df

data.frame with columns ID, CHROM, POS

gds

GenomicDataStream of reference panel

window

size of window in bp

flankWidth

additional window added to region

ids

variant IDs to evaluate imputation r2 score

method

method used to estimate shrinkage parameter lambda. default is "decorrelate"

lambda

(default: NULL) value used to shrink correlation matrix. Only used if method is "decorrelate"

quiet

suppress messages

...

additional arguments passed to impute_region()

Value

tibble storing imputed results:

ID

variant identifier

r2.pred

metric of accuracy of the imputed z-statistic based on its variance

lambda

shrinkage parameter

maf

minor allele frequency in reference panel

nVariants

number of variants used in imputation

Details

Implements method by Pasaniuc, et al. (2014).

References

Pasaniuc, B., Zaitlen, N., Shi, H., Bhatia, G., Gusev, A., Pickrell, J., ... & Price, A. L. (2014). Fast and accurate imputation of summary statistics enhances evidence of functional enrichment. Bioinformatics, 30(20), 2906-2914.

See also

run_imputez()

Examples

library(GenomicDataStream)
library(tidyverse)

# VCF file for reference
file <- system.file("extdata", "test.vcf.gz", package = "GenomicDataStream")

# initialize data stream
gds <- GenomicDataStream(file, "DS", initialize=TRUE)

# read file of variant locations
file <- system.file("extdata", "test.map", package = "GenomicDataStream")
df = read_tsv(file, show_col_types=FALSE)

# evaluate imputation r2 for these variants
ids = df$ID[c(1,9)]

# Given GenomicDataStream of reference panel,
# compute imputation r2 for variants in ids
run_imputability(df, gds, 10000, 1000, ids)
#> # A tibble: 2 × 5
#> # Groups:   ID [2]
#>   ID                r2.pred lambda   maf nVariants
#>   <chr>               <dbl>  <dbl> <dbl>     <int>
#> 1 1:10000:C:A 0.00000000238  1.000 0.347         8
#> 2 1:18000:C:G 0.00000000137  1.000 0.265         8