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  3. A Practical Framework For Gt-seq Panel Optimization.
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  3. A Practical Framework For Gt-seq Panel Optimization.

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Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy
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Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

Published on: August 25, 2019

A Practical Framework for GT-Seq Panel Optimization.

Chandika Rg1, Caitlin N Ott-Conn2, Peter T Euclide3

  • 1Department of Biological Sciences, University of Wisconsin-Milwaukee, Milwaukee, Wisconsin, USA.

Molecular Ecology Resources
|June 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed an iterative workflow to optimize Genotyping-in-thousands by sequencing (GT-seq) panels for robust genetic analysis. This systematic approach significantly improves genotyping success rates for diverse populations and non-model species.

Keywords:
Odocoileus virginianusGTseqGT‐seq panel optimizationOVSNP60SNPsWTD GT‐seq panelgenetic resourceheterozygositymultipurpose panel developmentpopulation genetics

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High-throughput Identification of Gene Regulatory Sequences Using Next-generation Sequencing of Circular Chromosome Conformation Capture (4C-seq)

Published on: October 5, 2018

Area of Science:

  • Genomics
  • Population Genetics
  • Conservation Biology

Background:

  • Genotyping-in-thousands by sequencing (GT-seq) panels are essential for ecological and evolutionary studies.
  • Current optimization methods for GT-seq panels lack formalization, hindering reproducibility.

Purpose of the Study:

  • To present a formalized, iterative workflow for optimizing GT-seq panels.
  • To improve the performance and reliability of GT-seq panels across various populations and research contexts.

Main Methods:

  • Developed an iterative optimization framework emphasizing systematic refinement and quality control.
  • Applied the framework to develop and optimize a GT-seq panel for white-tailed deer.
  • Utilized bioinformatic filtering and adjusted PCR conditions to select high-performing markers.

Main Results:

  • Optimized a GT-seq panel from 646 loci to 508 high-performing markers.
  • Increased the proportion of SNPs with >70% genotype rate from 25.5% to 87.8%.
  • Enhanced the overall genotype rate from 39.4% to 84% through iterative refinement.

Conclusions:

  • The presented iterative workflow provides a reproducible framework for GT-seq panel development.
  • Prioritizing optimization is crucial for generating robust, high-throughput genotyping tools in non-model species.
  • The workflow ensures consistent performance and maximizes data quality and utility for genetic studies.