Exact Inference for Hardy-Weinberg Proportions with Missing Genotypes: Single and Multiple Imputation

Area of Science:

• Population genetics
• Statistical genetics
• Bioinformatics

Background:

• Hardy-Weinberg equilibrium is a fundamental concept in population genetics.
• Missing genotype data can introduce bias in statistical inference.
• Discarding missing data can lead to inaccurate conclusions about genetic equilibrium.

Purpose of the Study:

• To develop statistical tests for Hardy-Weinberg equilibrium that account for missing genotype data.
• To evaluate the impact of imputation methods on Hardy-Weinberg equilibrium inference.
• To assess how missing data affects statistical conclusions in genetic studies.

Main Methods:

• Utilized exact-test based statistical inference for Hardy-Weinberg equilibrium.
• Developed tests incorporating inbreeding coefficients (or chi-squared statistics) and exact p-values.
• Applied single and multiple imputation techniques to handle missing genotype data.

Main Results:

• Exact inference on Hardy-Weinberg equilibrium is significantly altered by accounting for missing data.
• Imputation methods tended to reduce evidence for Hardy-Weinberg disequilibrium in markers with high missing rates (>5%).
• 6-13% of test results qualitatively changed at the 5% significance level depending on the imputation method.

Conclusions:

• Statistical inference for Hardy-Weinberg equilibrium must account for missing genotype data.
• Imputation strategies can mitigate bias caused by missingness, but may alter statistical significance.
• Careful consideration of missing data handling is crucial for accurate genetic association studies.