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Effect of diversity and missing data on genetic assignment with RAD-Seq markers
Balaji Chattopadhyay1, Kritika M Garg, Uma Ramakrishnan
1Ecology and Evolution, National Centre for Biological Sciences, TIFR, Bellary Road, Bangalore 560065, India. balaji.chattopadhyay@gmail.com.
BMC Research Notes
|November 27, 2014
Summary
Reduced representation libraries, like RAD-Seq, can have missing data. Our study shows adjusting parameters in STACKS and allowing more missing data improves SNP discovery and individual assignment power.
Area of Science:
- Population genetics
- Genomic analysis
- Bioinformatics
Background:
- Reduced representation libraries are crucial for population genetic studies.
- RAD-Seq data frequently contains substantial missing data.
- SNP calling algorithms may overlook biological variation.
Purpose of the Study:
- To assess the impact of biological diversity on SNP discovery using STACKS.
- To evaluate the influence of missing data on individual assignment with STRUCTURE.
- To optimize parameters for SNP mining in population genetics.
Main Methods:
- Utilized STACKS software to mine SNPs from RAD-Seq data.
- Adjusted diversity parameters within STACKS.
- Incorporated varying percentages of missing data for analysis.
- Employed STRUCTURE for individual assignment analysis.
Main Results:
- Diversity parameters in STACKS significantly affected the number of discovered SNPs.
- Higher percentages of missing data led to the retrieval of more loci.
- Increased missing data potentially enhanced the power for individual assignment.
Conclusions:
- Parameter choice in SNP discovery tools like STACKS is critical.
- Handling missing data in RAD-Seq analysis can improve population genetic inferences.
- Optimizing missing data thresholds can increase the utility of reduced representation sequencing for genetic studies.
