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Summary of scan1 object

Source: R/summary_scan1.R
summary_scan1.Rd

Summary of scan1 object

Usage

summary_scan1(
  object,
  map,
  snpinfo = NULL,
  lodcolumn = seq_len(ncol(object)),
  chr = names(map),
  sum_type = c("common", "best"),
  drop = 1.5,
  show_all_snps = TRUE,
  ...
)

# S3 method for class 'scan1'
summary(object, ...)

Arguments

object

object from scan1

map

A list of vectors of marker positions, as produced by insert_pseudomarkers.

snpinfo

Data frame with SNP information with the following columns (the last three are generally derived from with index_snps):

  • chr - Character string or factor with chromosome

  • pos - Position (in same units as in the "map" attribute in genoprobs.

  • sdp - Strain distribution pattern: an integer, between 1 and \(2^n - 2\) where \(n\) is the number of strains, whose binary encoding indicates the founder genotypes

  • snp - Character string with SNP identifier (if missing, the rownames are used).

  • index - Indices that indicate equivalent groups of SNPs.

  • intervals - Indexes that indicate which marker intervals the SNPs reside.

  • on_map - Indicate whether SNP coincides with a marker in the genoprobs

lodcolumn

one or more lod columns

chr

one or more chromosome IDs

sum_type

type of summary

drop

LOD drop from maximum

show_all_snps

show all SNPs if TRUE

...

other arguments not used

Value

tbl summary

Author

Brian S Yandell, brian.yandell@wisc.edu

Examples

# read data
iron <- qtl2::read_cross2(system.file("extdata", "iron.zip", package="qtl2"))
# insert pseudomarkers into map
map <- qtl2::insert_pseudomarkers(iron$gmap, step=1)

# calculate genotype probabilities
probs <- qtl2::calc_genoprob(iron, map, error_prob=0.002)

# grab phenotypes and covariates; ensure that covariates have names attribute
pheno <- iron$pheno
covar <- match(iron$covar$sex, c("f", "m")) # make numeric
names(covar) <- rownames(iron$covar)
Xcovar <- qtl2::get_x_covar(iron)

# perform genome scan
out <- qtl2::scan1(probs, pheno, addcovar=covar, Xcovar=Xcovar)

# summary
summary(out, map)
#> # A tibble: 40 × 5
#>    pheno  chr     pos marker       lod
#>    <chr>  <fct> <dbl> <chr>      <dbl>
#>  1 liver  1      90.3 c1.loc90   2.53 
#>  2 spleen 1      27.3 D1Mit18    0.949
#>  3 liver  2      56.8 D2Mit17    4.86 
#>  4 spleen 2      55.3 c2.loc55   1.83 
#>  5 liver  3      25.1 D3Mit22    1.08 
#>  6 spleen 3      42.1 c3.loc42   0.526
#>  7 liver  4      10.9 D4Mit2     2.74 
#>  8 spleen 4      53.6 D4Mit352   1.71 
#>  9 liver  5      55.5 c5.loc55.5 0.790
#> 10 spleen 5      62.3 D5Mit30    1.99 
#> # ℹ 30 more rows