A Pangenomic Method for Establishing a Somatic Variant Detection Resource in HapMap Mixtures.

Area of Science:

• Genetics
• Genomics
• Bioinformatics

Background:

• Somatic mosaicism plays a critical role in human health and disease.
• Existing benchmarks for somatic variant detection are limited, hindering research progress.

Purpose of the Study:

• To develop a robust, technology-agnostic benchmarking resource for somatic variant detection.
• To enable systematic evaluation and improvement of tools used in somatic mosaicism research.

Main Methods:

• Artificial somatic variants were generated by mixing HapMap cell lines.
• A pangenome graph approach was used to create a unified benchmarking set (>6M SNVs, 1.8M indels, 49K SVs, 10K MEIs).
• Ultra-deep simulated reads and a binomial model were employed for variant validation and coverage estimation.

Main Results:

• A large, diverse dataset of somatic variants across various chromosomes was established.
• CHM13 alignment demonstrated superior structural variant detection compared to GRCh38, especially in challenging genomic regions.
• Low detection rates were associated with repetitive genomic regions like centromeres and satellite sequences.

Conclusions:

• The developed resource provides an accurate and versatile platform for evaluating somatic variant detection technologies.
• Improved reference genomes and methods are crucial for accurate variant detection in complex genomic regions.
• This work facilitates advancements in understanding somatic mosaicism in human biology and disease.