LoGicAl: Local ancestry and genotype calling uncertainty-aware ancestry-specific allele frequency estimation from admixed samples.

LoGicAl improves accuracy of ancestry-specific allele frequency estimates in admixed samples by modeling uncertainty from local ancestry and genotype calling.

• Why it matters: Accurate estimation of allele frequencies in ancestral populations is crucial for understanding disease genetics, GWAS interpretation, and demographic history, but current methods suffer from biases due to unmodeled uncertainties.
• What they did: LoGicAl is a likelihood-based method that accounts for uncertainty in local ancestry inference, genotyping, and phasing, using an accelerated fixed-point algorithm to enhance scalability and efficiency, tested on sequence and array data from the 1000 Genomes Project.
• The result: LoGicAl reduces estimation errors and provides more precise, rapid ancestry-specific allele frequency estimates, enabling finer-scale analysis of genetic variation in admixed populations.