sequoia (version 3.0.3)

Err_RADseq: Convert Genotyping Error Rates from per-allele to per-locus

Description

Convert per-allele genotyping rates at homozygous (E0) and heterozygous (E1) sites to a length-3 vector with per-locus error rates hom|hom, het|hom, hom|het.

Usage

Err_RADseq(E0 = 0.005, E1 = 0.05, Return = "vector")

Value

Depending on Return, either:

• 'vector': a length 3 vector, with the probabilities (observed given actual) hom|other hom, het|hom, and hom|het.

• 'matrix': a 3x3 matrix, with probabilities of observed genotypes (columns) conditional on actual (rows)

Arguments

E0

per-allele genotyping rates at homozygous sites

E1

per-allele genotyping rates at heterozygous sites

Return

output format, 'vector' (default) or 'matrix'

Details

Estimation of per-allele genotyping rates is described in Bresadola et al (2020) - 'Estimating and accounting for genotyping errors in RAD-seq experiments', MER. The error model implemented here is identical to that in Table 1 of that paper, and the default values are also taken from that paper.

For further information on how the sequoia package handles genotyping errors, see ErrToM.

Examples

# Compare with default error pattern (SNP chip based) :
Err_RADseq(E0=0.001, E1=0.05)
ErrToM(0.05*(1-0.05)*2, Return='vector')

# usage in sequoia() and other functions:
Err_low <- Err_RADseq(E0=0.002, E1=0.05)
Err_high <- Err_RADseq(E0=0.01, E1=0.15)
if (FALSE) {
 SeqOUT_lowErr <- sequoia(GenoM, LHdata, Err=Err_low)
 SeqOUT_highErr <- sequoia(GenoM, LHdata, Err=Err_high)

# also usable for confidence estimates, and to explore potential consequences
# of the actual genotyping error rate being much higher/lower than assumed
EC <- EstConf(best_pedigree, LHdata, args.sim=list(SnpError=Err_high),
         args.seq=list(Err=Err_low))
}