R script analysing variants described in the paper

The file contains R code that loads the data from the R package HardyWeinberg. Functions are called that analyse the data and reproduce the tables given in the paper

Simulation results.

<p>Overall percentage of missing data, percentage of SNPs with missings, probabilities of missingness for the three genotypes and the root mean squared error (RSME) for the inbreeding coefficient () when missings are discarded, imputed by MICE or imputed by IMPUTE2, under MCAR and MNAR.</p

Intensity plot of a G/T polymorphism for 146 individuals.

<p>Missing values (NA, 33% of the data) indicated by black crosses occur mainly at the boundaries of homozygotes and heterozygotes.</p

Scatter plot of inbreeding coefficients for 1070 non-monomorphic SNPs with missings obtained by multiple imputation (MICE) and single imputation (IMPUTE2).

<p>Scatter plot of inbreeding coefficients for 1070 non-monomorphic SNPs with missings obtained by multiple imputation (MICE) and single imputation (IMPUTE2).</p

Estimation of inbreeding coefficients by multiple imputation and by omitting missings.

<p>Left panel: using allele intensities only. Right panel: using allele intensities and covariate SNPs in LD (complete and incomplete) with . Symbols indicate the result of two significance tests: a test for HWP discarding missings and a test for HWP with imputation of missings. Circles: SNPs with both tests non-significant; Diamonds: SNPs with both tests significant; Upward triangles: SNPs with a significant chi-square test when missings are omitted, but an insignificant test when missings are imputed. Downward triangles: SNPs with a non-significant chi-square test when missings are omitted, but a significant test when missings are imputed.</p

Significance tests of equal mean intensities for missing and non-missing genotyping results.

<p>Number and percentage of significance tests are given for 140 non-monomorphic SNPs with between 10 and 50% missing values (). Results are given for tests with and without homocedasticity assumption ( is the intensity variance of the completely observed genotypes, is the intensity variance of the missing genotypes).</p

Ternary plots of <i>m</i> = 50 imputed data set for the G/T polymorphism of Figure 1.

<p>Curves in the ternary plots indicate the HW parabola, and the limits of the 95% acceptance region of a test for HWP. Left panel: imputed data sets with allele intensities as covariates (model 3). Right panel: imputed data sets with allele intensities and 1 covariate SNP (model 5).</p

Number of imputed SNPs, number and percentage of significant SNPs with missings imputed, mean, median and maximum of the fraction of missing information () for multinomial logit models with five different sets of predictors.

<p>The last column (% reversal) indicates the percentage of SNPs whose test results changed status (from significant to non-significant or the reverse) in comparison with a test omitting missings.</p

Ternary plots and Q-Q plots for Hardy-Weinberg proportions.

<p>Curves in the ternary plots indicate the HW parabola, and the limits of the 95% acceptance region of a test for HWP. Top row plots are for 545 fully observed SNPs. Bottom row plots are for 140 SNPs with 10 to 50% missings (missings were discarded in these plots). The Q-Q plots show two lines, a solid reference line and an estimate of the linear tendency in the cloud of points (dashed).</p

Inbreeding coefficients, confidence intervals, <i>p</i>-values and missing data statistics (relative increase in variance (), and fraction of missing information ()) for multiple imputation with different multinomial logit models, and for single imputation with IMPUTE2.

<p>Inbreeding coefficients, confidence intervals, <i>p</i>-values and missing data statistics (relative increase in variance (), and fraction of missing information ()) for multiple imputation with different multinomial logit models, and for single imputation with IMPUTE2.</p