Sample Size Impact (SaSii): An R script for estimating optimal sample sizes in population genetics and population...

Area of Science:

• Population Genetics
• Genomic Studies
• Bioinformatics

Background:

• Acquiring large sample sizes for genetic studies presents significant challenges, including cost, time, and potential for biased results from small sample sizes.
• Existing guidelines for minimum sample size are not universally applicable across all study designs.
• Accurate sample size determination is essential for robust and reliable genetic research outcomes.

Purpose of the Study:

• To introduce SaSii (Sample Size Impact), an R script designed to assist researchers in defining minimum sample sizes for genetic studies.
• To provide empirical patterns and suggested minimum sample sizes derived from data analysis to aid in experiment design.
• To simplify the process of sample size estimation without requiring programming, extensive lab work, or specialized population genetics software.

Main Methods:

• Development of an R script named SaSii (Sample Size Impact) for sample size estimation.
• Analysis of empirical and simulated data using SaSii to identify patterns related to sample size requirements.
• Examination of previously published genotype datasets to derive practical patterns for population genetics and genomic studies.

Main Results:

• SaSii enables estimation of adequate sample sizes that accurately represent populations, simplifying experimental design.
• Empirical data analysis revealed patterns suggesting minimum sample sizes for various genetic analyses.
• Minimum sample sizes for single-nucleotide polymorphism (SNP) analysis were generally found to be smaller than for simple sequence repeat (SSR) analysis.

Conclusions:

• The SaSii script offers a user-friendly approach to determining appropriate sample sizes, reducing complexity in genetic research.
• The identified patterns from empirical datasets serve as valuable starting points for designing population genetics and genomic experiments.
• The study highlights differences in sample size needs between SNP and SSR analyses, contributing to more efficient study planning.