For that purpose, we sequentially excluded these parameters in simulating genotypes, such that no missing genotypes were assumed or both error rates (ER1 and ER2) were assumed to be zero during the simulations (M ASTERB AYES, simgenotype function), and the parentage analysis was otherwise performed as above.
Here, we disregard true missingness and assume that all missing genotypes are attributable to allelic dropout.
For simplicity, we assumed here that imputation of the candidates' missing genotypes did not cause a loss in selection efficiency, which is not fully realistic and tends to favour the genomic scenarios in the economic comparisons.
A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase.
Scheet, P. & Stephens, M. A fast and flexible statistical model for large-scale population genotype data: applications to inferring missing genotypes and haplotypic phase.
The mutual intersection of all data sets yielded 19,372 diallelic, autosomal SNPs with experimentally determined genotypes (i.e., no imputation of missing genotypes was performed).
In addition, 39,033 SNPs were excluded owing to low genotyping (with > 5% missing genotypes per marker) and 198,553 SNPs, owing to minor allele frequency of < 1%.
The following filters were used: MAF of 0.05, Hardy-weinberg p = 0, exclude markers with >0.5 missing genotypes and less than 0.5 nonzero genotypes.
The genotypes were quality controlled by excluding SNPs with >10% duplicate error rate, >10% missing genotypes or significant deviations from Hardy Weinberg equilibrium (P-value ≤ 1 × 10−10).
The performance of the genotyping assay for each method was assessed using three criteria: missing genotypes (assay fail rates), incomplete genotypes (loci departing from HWE) and inaccurate genotypes (inconsistent genotypes across replicate samples).
