GtTR: Bayesian estimation of absolute tandem repeat copy number using sequence capture and high throughput...

Area of Science:

• Genomics
• Bioinformatics
• Human Genetics

Background:

• Tandem repeats are crucial genomic regions prone to variation, contributing to human diversity and complex diseases.
• Analyzing tandem repeats is challenging due to technical limitations in high-throughput sequencing.
• Genomic variation in tandem repeats impacts coding and regulatory regions.

Purpose of the Study:

• To develop a novel, cost-effective targeted sequencing approach for simultaneous analysis of hundreds of tandem repeats.
• To create a Bayesian algorithm (GtTR) for population-scale genotyping of tandem repeats.
• To improve the understanding of tandem repeat variation in complex diseases.

Main Methods:

• Developed a Bayesian algorithm (GtTR) combining long-read reference data with short-read counting.
• Utilized PacBio long-read sequencing for reference dataset generation.
• Validated genotyping accuracy using PCR sizing analysis.

Main Results:

• GtTR achieved high accuracy in estimating VNTR copy number, with improved performance using PCR reference data.
• Genotype resolution increased with sequencing depth, showing significant improvements at higher coverage.
• Sequencing-based genotype estimates showed strong correlation with PCR validation results.

Conclusions:

• The novel GtTR approach offers a cost-effective method for exploring tandem repeat variation in Genome-Wide Association Studies (GWAS).
• This method facilitates the discovery of previously unrecognized repeat variations relevant to complex diseases.
• Enhancing the accuracy of the reference dataset can further improve genotyping precision.