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Test Hardy-Weinberg equilibrium at each locus

Source: R/loci_hwe.R
loci_hwe.Rd

Return the p-value from an exact test of HWE.

Usage

loci_hwe(.x, .col = "genotypes", ...)

# S3 method for class 'tbl_df'
loci_hwe(.x, .col = "genotypes", mid_p = TRUE, ...)

# S3 method for class 'vctrs_bigSNP'
loci_hwe(.x, .col = "genotypes", mid_p = TRUE, ...)

# S3 method for class 'grouped_df'
loci_hwe(
  .x,
  .col = "genotypes",
  mid_p = TRUE,
  n_cores = bigstatsr::nb_cores(),
  block_size = bigstatsr::block_size(nrow(.x), 1),
  type = c("tidy", "list", "matrix"),
  ...
)

Arguments

.x

a vector of class vctrs_bigSNP (usually the genotypes column of a gen_tibble object), or a gen_tibble.

.col

the column to be used when a tibble (or grouped tibble is passed directly to the function). This defaults to "genotypes" and can only take that value. There is no need for the user to set it, but it is included to resolve certain tidyselect operations.

...

not used.

mid_p

boolean on whether the mid-p value should be computed. Default is TRUE, as in PLINK.

n_cores

number of cores to be used, it defaults to bigstatsr::nb_cores()

block_size

maximum number of loci read at once.

type

type of object to return, if using grouped method. One of "tidy", "list", or "matrix". Default is "tidy".

Value

a vector of probabilities from HWE exact test, one per locus

Details

This function uses the original C++ algorithm from PLINK 1.90.

Author

the C++ algorithm was written by Christopher Chang for PLINK 1.90, based on original code by Jan Wigginton (the code was released under GPL3).

Examples

example_gt <- example_gt("gen_tbl")

# For HWE
example_gt %>% loci_hwe()
#> [1] 0.7552448 0.6969697 0.5000000 0.6363636 0.6969697 0.6363636

# For loci_hwe per locus per population, use reframe
example_gt %>%
  group_by(population) %>%
  reframe(loci_hwe = loci_hwe(genotypes))
#> # A tibble: 18 × 2
#>    population loci_hwe
#>    <chr>         <dbl>
#>  1 pop1          0.6  
#>  2 pop1          0.6  
#>  3 pop1          0.5  
#>  4 pop1          0.667
#>  5 pop1          0.7  
#>  6 pop1          0.5  
#>  7 pop2          0.5  
#>  8 pop2          0.5  
#>  9 pop2          0.5  
#> 10 pop2          0.5  
#> 11 pop2          0.5  
#> 12 pop2          0.5  
#> 13 pop3          0.5  
#> 14 pop3          0.5  
#> 15 pop3          0.5  
#> 16 pop3          0.5  
#> 17 pop3          0.5  
#> 18 pop3          0.5