PRIMAL: Fast and accurate pedigree-based imputation from sequence data in a founder population.

Area of Science:

• Genetics
• Bioinformatics
• Population Genetics

Background:

• Founder populations and large pedigrees are valuable for genetic mapping due to cost-efficient study designs.
• Accurate genome imputation is crucial for understanding genetic variation and its impact on phenotypes.

Purpose of the Study:

• To introduce PRIMAL (PedigRee IMputation ALgorithm), a novel algorithm for pedigree-based genome phasing and imputation in founder populations.
• To demonstrate the efficiency and accuracy of PRIMAL in imputing genotypes and inferring parental origins.

Main Methods:

• Developed PRIMAL, incorporating a novel indexing strategy of Identity-By-Descent (IBD) segments using clique graphs.
• Applied PRIMAL to impute genomes of 1,317 individuals from 98 whole genome sequences.
• Combined pedigree-based and linkage disequilibrium (LD)-based imputation methods.

Main Results:

• Successfully imputed 87% of genotypes with >99% accuracy across all allele frequencies.
• Inferred the parental origin of 83% of alleles using IBD cliques.
• Enabled genotype reconstruction for deceased ancestors, facilitating comprehensive genetic analysis.

Conclusions:

• PRIMAL provides a fast and accurate method for genetic imputation in founder populations.
• The imputed dataset enables cost-effective research into rare and common variant contributions to human phenotypes and parental origin effects on disease risk.