We evaluated the posterior distribution of the residual correlation in the Bayesian linear regression model used for the instrumental variable analysis (see below): this yields point and interval estimates (posterior mean and 95% credible interval) that allow for missing genotypes and phase uncertainty.
To maximize our sample size without adding uncertainty, we evaluated the effect of missing genotypes on individual assignment test results.
In addition to observed genotypes, our approach allows estimation of missing genotypes, resulting in substantial increases in power when genotyping resources are limited.
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).
