KBeagle: An Adaptive Strategy and Tool for Improving Imputation Accuracy and Computation Time.

Area of Science:

• Genomics
• Bioinformatics
• Computational Biology

Background:

• Whole-genome sequencing (WGS) is crucial for uncovering genetic variation, but technical limitations leave large genomic segments ungenotyped.
• Genotype imputation is essential for filling these missing data gaps, with accuracy and speed being key performance metrics.
• Existing imputation methods face challenges with large datasets and complex genomic structures.

Purpose of the Study:

• To develop an accurate and efficient genotype imputation method.
• To enhance the identification of trait-associated single-nucleotide polymorphism (SNP) loci.
• To reduce computational time and improve imputation quality in genomic research.

Main Methods:

• Implemented the K-Means clustering algorithm and multithreading to group reference individuals.
• Developed a novel imputation strategy named KBeagle.
• Compared KBeagle-imputed datasets (KID) against Beagle-imputed datasets (BID) for accuracy and efficiency.

Main Results:

• KBeagle identified more SNP loci associated with traits compared to BID.
• KBeagle achieved significantly lower false discovery rates (FDRs) and Type I error rates.
• The KBeagle strategy improved imputation matching rates and reduced calculation time.

Conclusions:

• KBeagle offers an accurate and efficient genotype imputation method.
• This approach is particularly beneficial for livestock sequencing studies with strong genetic structure.
• KBeagle enhances the utility of WGS data by improving genotype imputation quality and speed.