High-throughput genotyping is essential for identifying and developing superior pig varieties. Molbreeding offers ready-to-use GBTS panels with multiple marker densities, compatible with diverse sample types such as blood, hair follicles, semen, and other tissues, providing cost-effective and reliable genotyping for genetic analysis, trait mapping, and breed improvement.
For projects with specific genotyping requirements, custom GBTS panels can be developed. Contact us to discuss a solution tailored to your breeding program.
- Genotyping by Targeted Sequencing (GBTS) Panels - Ready to use
*Reference genome: Sscrofa11.1
Note: All our ready-to-use GBTS panels can be customized with additional markers for specific traits or loci. To include your loci of interest, please contact us.
- Product Highlight
Various density designed for different applications
Our ready-to-use GBTS panels are designed with various, flexibly adjustable marker densities, from high to low, to cater to your diverse application scenarios, truly optimizing time and cost. We offer flexible marker number adjustment and upgrade services, ensuring your research remains at the forefront.
Simple sampling & Fast turn around time
Pig ear, tail are all accepted for sample processing and DNA extraction.
Only 14 days are needed from sample arriving to report.
- Service Process
- Report Visualization
GenoBaits® Porcine 50K Panel & 1K Panel
The Genobaits 50K and 1K panels were designed based on sequencing data from hundreds of pig breeds, including 32 Duroc, 86 Large White, 29 Erhualian, 94 Yorkshire, as well as pigs from both southern and northern China. A total of 52,000 probes form this porcine panel, which has been validated in commercial breeding lines.
The 1K panel includes 1,200 selected SNP markers, making it ideal for parental verification, pedigree correction, and breed composition analysis.
- Application Example
- Zhang Z, Xing S, Qiu A, et al. The development of a porcine 50K SNP panel using genotyping by target sequencing and its application. J Integr Agric. 2023;2095-3119. https://doi.org/10.1016/j.jia.2023.07.033
- Wang X, Shi S, Wang G, et al. Using machine learning to improve the accuracy of genomic prediction of reproduction traits in pigs. J Anim Sci Biotechnol. 2022;13(1):60 https://doi.org/10.1186/s40104-022-00708-0
