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Detect LD-Summary Statistic Mismatches

Source: R/sumstatsQc.R
ldMismatchQc.Rd

Unified wrapper for detecting outlier variants due to LD-summary statistic mismatches. Dispatches to either dentistSingleWindow or slalom based on the method argument.

Usage

ldMismatchQc(
  zScore,
  R = NULL,
  X = NULL,
  nSample = NULL,
  method = c("slalom", "dentist"),
  ldMethod = "sample"
)

Arguments

zScore

Numeric vector of z-scores.

R

Square LD correlation matrix. Provide either R or X.

X

Genotype matrix (samples x SNPs). With ldMethod = "sample" both methods work on the genotypes without forming the LD matrix; any other ldMethod computes LD via computeLd. nSample defaults to nrow(X).

nSample

Number of samples in the LD reference panel. Required when R is provided and method = "dentist"; inferred from X when X is provided.

method

Which QC method to run: "slalom" (default) or "dentist", or the matching constructor – SlalomParam / DentistParam – to configure it at the same time. The constructor carries the choice, so there is no separate options argument that could disagree with it.

ldMethod

Character string specifying the LD computation method when X is provided. One of "sample" (default), "population", or "gcta". Ignored when R is provided directly.

Value

A data frame with at least a logical outlier column indicating which variants are identified as outliers. The remaining columns depend on the method used.

See also

dentistSingleWindow, slalom, summaryStatsQc

Examples

data(eqtlRegionExample)
R <- cor(eqtlRegionExample$X[, 1:20])
ldMismatchQc(zScore = rnorm(20), R = R, nSample = 415)
#> # A tibble: 20 × 5
#>    original_z   prob pvalue outlier nlog10p_dentist_s
#>         <dbl>  <dbl>  <dbl> <lgl>               <dbl>
#>  1     1.30   0.0507 0.903  FALSE              0.534 
#>  2    -0.435  0.0492 0.332  FALSE              0.321 
#>  3    -0.809  0.0497 0.209  FALSE              0.0498
#>  4    -0.522  0.0493 0.301  FALSE              0.277 
#>  5    -0.288  0.0491 0.387  FALSE              0.159 
#>  6    -0.643  0.0495 0.260  FALSE              0.377 
#>  7     0.343  0.0492 0.634  FALSE              0.0838
#>  8     1.45   0.0511 0.926  FALSE              0.744 
#>  9     1.35   0.0508 0.912  FALSE              0.479 
#> 10     0.475  0.0493 0.683  FALSE              0.0177
#> 11    -0.249  0.0491 0.402  FALSE              0.144 
#> 12    -1.44   0.0511 0.0744 NA               NaN     
#> 13    -0.984  0.0500 0.163  FALSE              0.663 
#> 14    -1.19   0.0504 0.116  FALSE              1.06  
#> 15    -0.0605 0.0491 0.476  FALSE              0.0640
#> 16     1.95   0.0528 0.974  FALSE              1.18  
#> 17    -1.40   0.0509 0.0808 FALSE              1.34  
#> 18    -1.18   0.0504 0.119  FALSE              1.16  
#> 19    -0.243  0.0491 0.404  FALSE              0.0734
#> 20    -0.372  0.0492 0.355  FALSE              0.465