Plos iconPlosSep 18, 2026 ~1 min source read

Direct microhaplotype genotyping for GT-seq (Genotyping-in-Thousands by Sequencing) using a diploid abundance model

Finger GT-seq (Genotyping-in-Thousands by Sequencing) is widely used for high-throughput amplicon genotyping, but most analytical pipelines focus on single SNPs or rely on alignment-based variant calling. Here we present a direct microhaplotype genotyping framework that leverages the high read depth and low error rates typical of paired-end Illumina and Element sequencing.

Direct microhaplotype genotyping for GT-seq (Genotyping-in-Thousands by Sequencing) using a diploid abundance model

Share this story

Send the public story page.

Useful takeaways from this story.

Finger GT-seq (Genotyping-in-Thousands by Sequencing) is widely used for high-throughput amplicon genotyping, but most analytical pipelines focus on single SNPs or rely on alignment-based variant calling.

Here we present a direct microhaplotype genotyping framework that leverages the high read depth and low error rates typical of paired-end Illumina and Element sequencing.

The pipeline first identifies primer-bounded reads and resolves paired-end sequences into quality-aware consensus amplicon sequences.

Building the complete brief

The page is ready to read now. The fuller skim-friendly version will appear here automatically.

The useful part

Finger GT-seq (Genotyping-in-Thousands by Sequencing) is widely used for high-throughput amplicon genotyping, but most analytical pipelines focus on single SNPs or rely on alignment-based variant calling. Here we present a direct microhaplotype genotyping framework that leverages the high read depth and low error rates typical of paired-end Illumina and Element sequencing. The pipeline first identifies primer-bounded reads and resolves paired-end sequences into quality-aware consensus amplicon sequences.

How it works

  • Comparison with an independent BWA/BCFtools alignment-based workflow demonstrated 99.67% genotype concordance across 102,520 genotype comparisons spanning 1,085 SNPs in 96 individuals.
  • Within each sample and locus, unique sequences are ranked by read abundance and the top one or two sequences are retained as directly observed haplotypes.
  • These alleles are aggregated across samples to construct a catalog of observed haplotypes for each locus.
  • In a second pass, reads are assigned to catalog haplotypes by exact sequence matching to produce diploid genotypes.
  • Finally, catalog haplotype sequences are compared to identify phased SNP and collapsed indel variation.

What to take from it

Optionally, catalog haplotypes may be aligned to a reference genome to project observed variants onto genomic coordinates and generate standards-compliant VCF output. Genotype concordance remained above 99.4% even at the minimum supported sequencing depth of 10 reads per locus, demonstrating robust performance across a broad range of sequencing depths.

Keep reading in the app

Open the app view to save this story, compare related coverage, and continue from the same source.

Open in app