Parentage assignment with genotyping-by-sequencing data

Area of Science:

• Genetics
• Bioinformatics
• Population Genetics

Background:

• Traditional parentage assignment relies on array data.
• Genotyping-by-sequencing (GBS) offers a cost-effective alternative but presents challenges with low coverage and variable sequencing depth.
• Accurate genotype calling is crucial for parentage assignment.

Purpose of the Study:

• To evaluate the efficacy of genotype-by-sequencing (GBS) data for parentage assignment.
• To address challenges associated with low-coverage GBS data and variable sequencing depth.
• To develop and validate a novel probabilistic framework for parentage assignment using GBS data.

Main Methods:

• Extended a probabilistic framework to compare noisy GBS genotypes against proposal distributions of expected genotype probabilities for relatives.
• Developed the AlphaAssign program for parentage assignment.
• Evaluated the method using simulated data with varying GBS coverage levels.

Main Results:

• High-coverage (>2×) GBS data performed comparably to array data, requiring fewer markers for accurate parentage assignment.
• Low-coverage GBS data (as low as 0.1×) successfully assigned parentage when a large number of markers were utilized.
• A high false positive rate with low-coverage GBS data was observed when the true parent was absent from candidate lists, but this was reducible by adjusting the assignment threshold.

Conclusions:

• Genotyping-by-sequencing (GBS) is a viable and effective method for parentage assignment, even with low-coverage data.
• The developed AlphaAssign program provides a robust tool for parentage assignment using GBS data.
• Careful consideration of marker numbers and assignment thresholds is necessary for optimizing accuracy, especially with low-coverage GBS data.