Evidence map›Paper›PMID 42758791›Full record

ArticlePLoS computational biology2026

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

Nathan R Campbell, Amanda R Campbell, Shannon K Blair, Amanda J Finger

Abstract read
In one paragraph

Article in PLoS computational biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Nathan R CampbellGTseek LLC, Twin Falls, Idaho, United States of America.ORCID https://orcid.org/0009-0000-2072-4186
Amanda R CampbellGTseek LLC, Twin Falls, Idaho, United States of America.
Shannon K BlairUniversity of California - Davis, Davis, California, United States of America.ORCID https://orcid.org/0000-0002-2855-6487
Amanda J FingerUniversity of California - Davis, Davis, California, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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. 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. Optionally, catalog haplotypes may be aligned to a reference genome to project observed variants onto genomic coordinates and generate standards-compliant VCF output. This framework enables robust, microhaplotype genotyping directly from high-depth amplicon sequencing data. 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. 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.

Indexed as

Genotyping TechniquesHaplotypesHigh-Throughput Nucleotide SequencingSequence Analysis, DNAComputational BiologyDiploidyGenotypeHumansModels, GeneticPolymorphism, Single Nucleotide

Identifiers

PMID42758791
PMCPMC13607797

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.