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This paper presents an exact test for HWE that takes missing genotypes into account.
As noted by other studies (7), these findings emphasize the need for larger reference panels from which to impute missing genotypes into extremely large GWAS datasets.
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Missing genotypes in scenario 2 depend on the missing genotypes in scenario 1, i.e. all animals having missing genotypes in scenario 1 have missing genotypes in scenario 2 as well.
Any missing individual genotype was imputed using DAGPHASE [ 22] and Beagle [ 23], to avoid missing genotypes in the final data.
In practice, one also needs to take into account the effect of missing genotypes and genotyping errors.
SNPMStat v 3.0 simultaneously fits a model of association and imputes genotypes by integrating inference of missing genotypes and estimation of odds ratio into a single likelihood framework.
These methods first phase the genotype data and take the phased haplotype information into consideration when inferring missing genotypes.
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%.
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