Next Generation Sequencing of Pooled Samples: Guideline for Variants' Filtering

Area of Science:

• Population Genetics
• Genomic Sequencing Technologies
• Bioinformatics

Background:

• Next Generation Sequencing (NGS) costs are decreasing, but sequencing large populations remains economically challenging.
• Pool-sequencing (Pool-seq) offers a cost- and time-effective alternative by pooling DNA from multiple individuals.
• Accurate variant calling and allele frequency (AF) estimation are complicated by DNA pooling, with sequencing errors potentially mimicking low-frequency variants.

Purpose of the Study:

• To evaluate the robustness and reliability of allele frequency (AF) estimates derived from Pool-seq data.
• To develop and validate a filtering strategy for removing spurious variants in Pool-seq experiments.
• To provide a practical guideline for improving variant accuracy in Pool-seq data analysis.

Main Methods:

• Targeted re-sequencing of 996 individuals organized into 83 DNA pools (12 individuals per pool).
• Comparison of Pool-seq AFs against public variant databases and individual SNP-genotyping data.
• Development of a filtering guideline based on the Kolmogorov-Smirnov statistical test for variant filtering.

Main Results:

• Pool-seq AFs demonstrated robustness and reliability when compared to external and internal datasets.
• The proposed filtering guideline effectively removed a significant proportion of false positive variants.
• The filters successfully retained the majority of true positive variants, validating their efficacy.

Conclusions:

• Pool-seq is a reliable method for allele frequency estimation in population genetics.
• The developed Kolmogorov-Smirnov test-based filtering guideline enhances variant calling accuracy in Pool-seq data.
• This generic filtering approach is readily applicable to diverse Pool-seq studies, improving data quality.