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Updated: Jun 28, 2026

Semiconductor Sequencing for Preimplantation Genetic Testing for Aneuploidy

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

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 virginianus GTseq GT‐seq panel optimization OVSNP60 SNPs WTD GT‐seq panel genetic resource heterozygosity multipurpose panel development population genetics

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Published on: October 5, 2018

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

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.