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Evaluating information content of SNPs for sample-tagging in re-sequencing projects
Hao Hu1, Xiang Liu2, Wenfei Jin3
1Department of human molecular genetics, Max-Planck Institute for Molecular Genetics, Berlin, 14195, Germany.
Scientific Reports
|May 16, 2015
Summary
Accurate sample identification in re-sequencing is crucial. This study develops an optimized SNP panel for reliable sample tagging, requiring only 30 SNPs to distinguish 100,000 individuals, enhancing study integrity.
Area of Science:
- Genomics
- Bioinformatics
Background:
- Accidental sample mix-ups are a significant challenge in re-sequencing studies.
- Reliable sample identification is essential for data integrity and reproducibility.
Purpose of the Study:
- To develop a model for measuring Single Nucleotide Polymorphism (SNP) information content.
- To optimize SNP panels for maximal discrimination and accurate sample tagging.
Main Methods:
- Developed a computational model to quantify the information content of SNPs.
- Optimized SNP panels for efficient individual discrimination.
- Simulated large populations to test the robustness of the SNP tagging strategy.
Main Results:
- As few as 60 optimized SNPs can differentiate individuals in a global population.
- Approximately 30 optimized SNPs are sufficient to label up to 100,000 individuals.
- Simulations showed high average Hamming distances (>18) and low duality frequencies (<1 in 10,000) with 30 SNPs.
Conclusions:
- The optimized SNP panel strategy provides robust and cost-effective sample discrimination for re-sequencing.
- The developed SNP selection program allows customization for specific research needs, including Whole Exome Sequencing.
- This approach significantly improves the reliability of large-scale re-sequencing projects.
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