SnpStats {sequoia}

SNP Summary Statistics

Description

Estimate allele frequency (AF), missingness and Mendelian errors per SNP.

Usage

SnpStats(
  GenoM,
  Pedigree = NULL,
  Duplicates = NULL,
  Plot = TRUE,
  quiet = TRUE,
  ErrFlavour
)

Arguments

GenoM

genotype matrix, in sequoia's format: 1 column per SNP, 1 row per individual, genotypes coded as 0/1/2/-9, and row names giving individual IDs.

Pedigree

dataframe with 3 columns: ID - parent1 - parent2. Additional columns and non-genotyped individuals are ignored. Used to count Mendelian errors per SNP and (poorly) estimate the error rate.

Duplicates

dataframe with pairs of duplicated samples

Plot

show histograms of the results?

quiet

suppress messages

ErrFlavour

DEPRECATED AND IGNORED. Was used to estimate Err.hat

Details

Calculation of these summary statistics can be done in PLINK, and SNPs with low minor allele frequency or high missingness should be filtered out prior to pedigree reconstruction. This function is provided as an aid to inspect the relationship between AF, missingness and genotyping error to find a suitable combination of SNP filtering thresholds to use.

For pedigree reconstruction, SNPs with zero or one copies of the alternate allele in the dataset (MAF \le 1/2N) are considered fixed, and excluded.

Value

A matrix with a number of rows equal to the number of SNPs (=number of columns of GenoM), and when no Pedigree is provided 2 columns:

AF

Allele frequency of the 'second allele' (the one for which the homozygote is coded 2)

Mis

Proportion of missing calls

HWE.p

p-value from chi-square test for Hardy-Weinberg equilibrium

When a Pedigree is provided, there are 8 additional columns:

n.dam, n.sire, n.pair

Number of dams, sires, parent-pairs successfully genotyped for the SNP

OHdam, OHsire

Count of number of opposing homozygous cases

MEpair

Count of Mendelian errors, includes opposing homozygous cases when only one parent is genotyped

n.dups, n.diff

Number of duplicate pairs successfully genotyped for the SNP; number of differences. The latter does not count cases where one duplicate is not successfully genotyped at the SNP

See Also

GenoConvert to convert from various data formats; CheckGeno to check the data is in valid format for sequoia and exclude monomorphic SNPs etc., CalcOHLLR to calculate OH & ME per individual.

Examples

Genotypes <- SimGeno(Ped_HSg5, nSnp=100, CallRate = runif(100, 0.5, 0.8),
                     SnpError = 0.05)
SnpStats(Genotypes)   # only plots; data is returned invisibly
SNPstats <- SnpStats(Genotypes, Pedigree=Ped_HSg5)