MicroDrop

MicroDrop estimates allelic dropout rates and corrects bias in microsatellite genotype data to improve inference of heterozygosity and inbreeding from nonreplicated PCR-amplified samples.

Key Features:

• Estimation of Allelic Dropout Rates: Uses a maximum-likelihood framework with an expectation-maximization algorithm to estimate allelic dropout rates from nonreplicated genotype data, accounting for sample-specific and locus-specific factors.
• Correction of Bias in Genetic Estimates: Adjusts observed heterozygosity and inbreeding estimates by accounting for misclassification of heterozygotes as homozygotes due to allelic dropout.
• Imputation of Missing Genotypes: Generates imputed genotype datasets by sampling missing genotypes from an allele frequency model with optional inclusion or exclusion of Hardy-Weinberg equilibrium.
• Robustness to Model Assumptions: Maintains performance under violations of model assumptions such as population structure and genotyping errors unrelated to allelic dropout.

Scientific Applications:

• Population genetics: Improves estimation of genetic diversity, heterozygosity, and population structure from microsatellite data.
• Molecular ecology: Supports ecological and conservation genetic studies that rely on microsatellites and limited DNA samples.
• Nonreplicated/low-template genotyping studies: Enables analysis of single-pass PCR-amplified microsatellite datasets by estimating dropout and imputing missing genotypes without replicate genotyping.

Methodology:

Parameter estimation uses maximum likelihood and an expectation-maximization algorithm to estimate dropout rates and allele frequencies from single genotype datasets; bias correction and imputation sample missing genotypes from an allele frequency model (with optional Hardy-Weinberg equilibrium) and apply multiple imputations to adjust heterozygosity and inbreeding estimates.