YHap: a population model for probabilistic assignment of Y haplogroups from re-sequencing data

Area of Science:

• Genetics
• Bioinformatics
• Population Genetics

Background:

• Y haplogroup analysis is crucial for genealogy, population genetics, medical genetics, and forensics.
• Advancements in high-throughput sequencing generate low-coverage data, posing challenges for Y haplogroup assignment.
• Existing methods for Y haplogroup assignment are often optimized for high-coverage data.

Purpose of the Study:

• To develop a novel algorithm for accurate Y haplogroup assignment using low-coverage population sequence data.
• To address the limitations of current methods in handling low-coverage Y chromosome sequencing data.

Main Methods:

• Developed the YHap algorithm, employing an imputation framework.
• Jointly predicted Y chromosome genotypes and assigned Y haplogroups.
• Utilized low-coverage population sequence data.

Main Results:

• YHap demonstrates accurate Y haplogroup assignment capabilities.
• The algorithm performs effectively with less than 2x coverage sequencing data.
• Validation was performed using data from the 1000 Genomes Project.

Conclusions:

• An imputation framework effectively borrows information across multiple samples within a population.
• This approach enables accurate Y haplogroup assignment even with low-coverage data.
• YHap offers a robust solution for Y haplogroup determination in population genetic and forensic applications.